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    OFFICIAL WEBPAGE

    JEAN-MARIE SOURIAU

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    In May 1969 the groundbreaking book of Jean-Marie Souriau appeared, Structure des Systèmes Dynamiques. We will celebrate, in 2019, the jubilee of its publication, with a conference in honour of the work of this great scientist.
    Welcome to the conference !

    LOCATION
    MAP

    Paris-Diderot Université
    4 Rue Elsa Morante, 75013 Paris

    REGISTRATION

    Just send us an email with your name and your affiliation (if any)…

    SPEAKERS

    • Frédéric Barbaresco (Thales Group, France)
    • Daniel Bennequin (Université Paris Diderot, France)
    • Jean-Pierre Bourguignon (European Research Council, Europe)
    • Pierre Cartier (IHES Paris, France)
    • Maurice Courbage (Université Paris Diderot, France)
    • Dan Christensen (University of Western Ontario, Canada)
    • Thibault Damour (IHES Paris, France)
    • Paul Donato (Aix-Marseille Université, France) Souriau, a synthesis of his work
    • Paolo Giordano (Wolfgang Pauli Institute, Wien, Austria)
    • Serap Gürer (Galatasaray Üniversitesi, Turkey) Differential Forms on Stratified Spaces
    • Patrick Iglesias-Zemmour (Aix-Marseille Université, France) Symplectic Diffeology. Dissipative Thermodynamics in General Relativity
    • Yael Karshon (University of Toronto, Canada)
    • Yvette Kosmann-Schwarzbach (Paris, France)
    • Marc Lachieze-Rey (APC — Université Paris Diderot, France)
    • Martin Pinsonnault (University of Western Ontario, Canada)
    • Elisa Prato (Università degli Studi di Firenze, Italy)
    • Urs Schreiber (Czech Academy of the Sciences, Czech Republic)
    • Jedrzej Sniatycki (University of Calgary, Canada)
    • Roland Triay (Aix-Marseille Université, France)
    • San Vũ Ngọc (Université de Rennes, France)
    • Jordan Watts (Central Michigan University, USA)
    • Alan Weinstein (University of California, Berkeley, USA)
    • Enxin Wu (汕头大学Shantou University, China)

    POSTERS

    If you want to present a poster, just send us an email with your name, affiliation, the title of your poster and possibly a link on a pdf.

    SYLLABUS

    In May 1969 the groundbreaking book of Jean-Marie Souriau appeared, Structure des systèmes dynamiques. We will celebrate, in 2019, the jubilee of its publication, with a conference in honour of the work of this great scientist.

    The influence of Souriau’s work is felt in the areas he has innovated or developed in his own way. It is important to take stock of it, in particular in order to make the current and future generations aware of his original and deep thought.

    The main reasons for organising this conference are the singularity, and at the same time the scope, of the work of Souriau. He was able to create, in his time, a homogeneous group of “Souristes” who for the most part have reached maturity and are able today to convey the originality of this work. It is also time to take stock of the important work to which it has given rise among foreign researchers, many of whom we will invite to speak. The work of Jean-Marie Souriau lives on in different areas of the scientific world and, at different levels of depth, in the development of mathematics and physics, and therefore according to the different temporalities of the history of these disciplines.

    All scholars, old and new, recognize this. Souriau’s work is particularly important work for the relationships he has established and developed between physics and geometry. He is one of the most important founders of symplectic geometry, and the theoretical exploitation of his work in this field is far from exhausted.

    André Lichnerowicz has said that his work could belong to four international scientific unions: mathematics, mechanics, physics and astronomy. But what interests us is not only Souriau the scientist, but also the philosopher. What is striking is the unity of his thought through the variety of his fields of interest. It is likely true that this thought grows deeper as its areas of application expand. The object of our inquiry will be to analyse his thought insofar as it is related to the philosophy of science and even to pure philosophy.

    This conference aims to review the entire work of Jean-Marie Souriau in the five areas in which he worked.

    1. Symplectic mechanics. Symplectic structure of the space of the movements of a dynamic system, action of invariance groups, moment map, symplectic cohomology, barycentric decomposition theorem. Elementary systems (particles): homogeneous symplectic variety, classification by coadjoint orbits.

    2. Geometric Quantization. Prequantization condition, pre-quantum bundle, polarizations, quantification of coadjoint orbits.

    3. Thermodynamics. Geometric statistical equilibria in symplectic manifolds. Vector temperature and thermodynamic dissipative model in general relativity.

    4. General Relativity and Cosmology. General Covariance Principle, Robertson-Walker universe model with cosmological constant.

    5. Diffeology. Renewal of the formal framework of differential geometry by a stable space category by all natural set operations (i.e. complete, complete, Cartesian closed). This includes highly singular spaces that may not even be separated, infinite dimensional spaces, and so on.

    6. Philosophy of Science, Epistemology, History of Science. History of each of the domains evoked (symplectic mechanics, quantification, cosmology and relativity, thermodynamics). In each of these areas Souriau introduced new ideas. These new views have not ceased to be relevant and fruitful. The task of a philosophy of science will be to highlight this novelty.

    As far as philosophy is concerned, a new question has been raised about the importance of this work, in its variety and unity, and in its impact on the philosophy of science and philosophy in general, which we propose to deal with in this conference. Souriau’s originality manifested itself in his will and in his attempts to create new languages. We will analyze, in the philosophy section, this important aspect of his work, most evident in his work in geometry and relativity.

    The Organizers: J.-J. Szczeciniarz & P. Iglesias-Zemmour

    SPONSORS

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    OFFICIAL WEBSITE

    Presentation

    A seminar on Topological and Geometrical Structures of Information has been organized at CIRM in 2017, to gather engineers, applied and pure mathematicians interested in the geometry of information. This year FGSI’19 conference will be focused on the foundations of geometric structures of information. It is dedicated to the triumvirat Cartan - Koszul - Souriau and their influence on the field.

    The conference will take place in Montpellier from Monday 4th February 2019 at 9am until Wednesday 6th February at 1pm.

    Poster
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    Speakers

    • Anton ALEKSEEV (Geneva Univ.)
    • Dmitri ALEKSEEVSKY (Moscow IITP)
    • John BAEZ (Riverside UC)
    • Michel BRION (Grenoble Univ.)
    • Misha GROMOV* (Paris IHES)
    • Patrick IGLESIAS-ZEMMOUR (Marseille Univ.)
    • Yann OLLIVIER (Paris Facebook)
    • Vasily PESTUN (Paris IHES)
    • Aissa WADE (Penn State Univ.)
      *to be confirmed

    Panel sessions :

    • SYMPLECTIC GEOMETRY IN PHYSICS, led by Damien CALAQUE
    • TRIBUTE TO J-L KOSZUL AND J-M SOURIAU, led by Frederic BARBARESCO and Michel N'GUIFFO-BOYOM

    Registration
    https://fgsi2019.sciencesconf.org/registration/index

    Sponsors

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    Abstract
    An overview over recently developed methods for proving decay to equilibrium for dissipative dynamical systems is presented. The methodology is based on Lyapunov functionals, often with the physical interpretation of a (generalized) entropy or free energy.
    The course features a short formal introduction to stochastic processes, aimed at an audience with a PDE background. The concepts of martingales and time reversal of homogeneous Markov processes are used to derive local decay results for relative
    entropies. These are applied to various examples of Levy processes, including applications in kinetic transport theory, in mathematical biology, and in chemical reaction networks. Quantitative decay results are derived from entropyentropy decay inequalities or from inequalities between entropy decay and its time derivative, i.e. by the celebrated Bakry-Emery approach.
    A focus is on hypocoercive problems, where decay to equilibrium holds despite the fact that the decay term for the natural entropy functionals is only semi-denite. Various recent approaches to such problems, mainly in kinetic theory, are compared and unied.
    Finally, examples of nonlinear problems are discussed, and the question of structural assumptions allowing for entropy decay is examined.

    Lundi 8 octobre 2018
    Lundi 15 octobre 2018
    Lundi 22 octobre 2018
    Lundi 29 octobre 2018

    Lundi 5 novembre 2018
    Lundi 12 novembre 2018
    Mercredi 21 novembre 2018
    Lundi 26 novembre 2018
    De 14h à 17h
    Institut Henri Poincaré - Salle 314
    *

    *et exceptionnellement salle 201 le 29/10/18
    11 rue Pierre et Marie 75005 Paris
    Information
    www.sciencesmaths-paris.fr

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    Chercheur/Ingénieur Sénior en Machine Learning
    PDF FILE OF THE ANNOUNCEMENT

    Sophia Antipolis, le 28/05/2018 : Depuis 2002, Median Technologies a élargi les limites de l'identification, de
    l'interprétation, de l'analyse et de la communication des données de l'imagerie dans le monde médical.
    Nous sommes au cœur des solutions innovantes de logiciels en imagerie, pour le développement clinique de
    médicaments, l’aide au diagnostic et le suivi des patients atteints de cancers. Nos clients sont les laboratoires
    pharmaceutiques, les sociétés de biotechnologie, ainsi que les autorités et les institutions de santé à travers le monde.
    Median Technologies se situe à l’intersection de la médecine, de l’imagerie médicale et des technologies de l’information
    et de la communication. Notre équipe, qui inclut aujourd’hui environ 130 collaborateurs en Europe, Asie et aux Etats-
    Unis, combine des expertises scientifiques, techniques, médicales et réglementaires pour développer un logiciel
    innovant d’analyse en imagerie médicale, dans le but d’améliorer la vie des patients atteints de maladies, dans le monde
    entier. Nous sommes guidés par nos valeurs fondamentales : l’innovation de pointe comme objectif, de la qualité dans
    tous ce que nous faisons, du soutien envers nos clients dans la réalisation de leurs objectifs et en nous rappelant de
    toujours faire passer le patient en premier.
    Basés sur la côte d’Azur, avec une filiale sur la côte est des Etats-Unis, nous travaillons dans un contexte international
    et multiculturel particulièrement attractif et épanouissant.

    Dans le cadre de notre recherche et développement en Intelligence Artificielle appliquée à l’imagerie médicale, nous
    recherchons : Chercheur/Ingénieur Sénior en Machine Learning, H/F.
    Intégré dans une équipe multidisciplinaire de recherche et développement dans le cadre du projet iBIOPSY®, vous
    serez le Chercheur/Ingénieur Senior dans la recherche et le développement de solutions d’imageries médicales
    innovantes à l'aide de l'apprentissage par machine et autres méthodes d’IA.
    L'imagerie médicale est l'un des champs les plus prospère dans l'apprentissage par machine. Nous recherchons un
    Scientifique Sénior, enthousiaste, dynamique, organisé, avec une forte expérience ML et d'excellentes compétences en
    communication qui se développeront au cœur de l'innovation technologique.

    Présentation des activités et tâches principales associées au poste
    o Poste rattaché au service Machine Learning and Imaging, sous la responsabilité du Chief Scientific
    Officer
    o Responsabilités :

    1. Vous appliquerez vos connaissances en IA / ML pour développer des solutions innovantes et
      construirez des prototypes de faisabilité en utilisant des données provenant de systèmes
      d'imagerie médicale tels que les IRM et les tomodensitomètres (TDM).
    2. Votre travail comprendra la recherche agile et le développement de nouveaux algorithmes et
      systèmes d'apprentissage automatique. Faisant partie de notre centre d'innovation frontale,
      vous serez activement impliqué dans la recherche, le suivi, l'évaluation et à la mise à profit des
      techniques de rupture, ainsi que des nouvelles tendances industrielles, académiques et
      technologiques.
    3. En outre, vous serez associé au transfert de la technologie et partagerez votre vision avec les
      équipes d'innovation. Vous générerez de la propriété intellectuelle pour l'entreprise. Vous serez
      appelé à rédiger des articles évalués par des pairs, et à présenter des résultats lors de
      conférences industrielles / scientifiques.
    4. Nous vous assisterons dans la création de solutions d'imagerie révolutionnaires reposant sur
      l'intelligence artificielle exploitant le cloud et appliquant des techniques modernes d'IA pour
      créer de la valeur à partir des référentiels d'imagerie et de données cliniques générés par nos
      partenaires de recherche médicale et pharmaceutique. Ces systèmes et services activés par
      l'IA iront au-delà de l'analyse d'image pour transformer la pratique médicale et le
      développement de médicaments.

    o Les responsabilités incluent également la gestion et l'engagement avec nos partenaires technologiques
    stratégiques tiers
    o Les responsabilités futures peuvent inclure la direction d'une équipe de scientifiques en apprentissage
    automatique.

    Profil sollicité
    o Formation : Master ou Doctorat en Mathématiques, Informatique, Ingénierie Electrique ou autre
    domaine connexe
    o Expérience :
    • Minimum 3 ans d’expérience pertinente en Apprentissage Machine
    • Expérience en gestion d'équipe souhaitée
    • Une expérience des réseaux générateurs tels que les GAN serait un atout
    • Auteur sur la recherche connexe
    • Grande expérience de travail avec les frameworks d'apprentissage automatique
    • Solide expérience des technologies opensource pour accélérer l'innovation
    o Connaissances :
    • Connaissance technique approfondie de l'IA, du Machine Learning automatique en vision par
    ordinateur ou dans un domaine connexe
    • Une forte connaissance du traitement des données statistiques, des techniques de régression,
    des réseaux de neurones, des arbres de décision, de la classification, de la reconnaissance
    des formes, des probabilités, des systèmes stochastiques, de l'inférence bayésienne, des
    techniques statistiques et de la réduction de la dimension, est fondamentale
    o Compétences/ Qualités
    • Solides compétences en programmation en R, Python, C ++ ou un autre langage de
    programmation
    • Solides compétences interpersonnelles, de communication et de présentation ainsi qu’une
    capacité à travailler dans une équipe internationale

    Eléments du contrat
    o Poste basé à : Sophia-Antipolis, France
    o Type de contrat : CDI
    o Date de début du contrat : au plus tôt
    o Rémunération : à négocier selon profil

    Avantages offerts par la société
    o Cadre épanouissant
    o Tickets restaurant
    o Restaurant d’entreprise
    o Mutuelle d’entreprise

    MEDIAN Technologies SA au capital de 583 794,45 €
    RCS Grasse – SIRET 443 676 309 00042 – APE 5829C
    Siège social : Les 2 Arcs – Bat B - 1800 route des Crêtes
    06560 Valbonne – France
    Téléphone : +33 (0)4 93 333 777 – Fax : +33 (0)4 92 90 65 99
    Web : www.mediantechnologies.com

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    Official WEBSITE

    Information about the research
    The Division of Applied Mathematics and Statistics at the Department of Mathematical Sciences at Chalmers University of Technology and the University of Gothenburg, together with the Agrifood and Bioscience unit of RISE Research Institutes of Sweden, invites applications for one two-year postdoctoral position starting November 1, 2018, or as agreed. The successful candidate will be offered a one-year employment at Chalmers, followed by a one-year employment at RISE Agrifood and Bioscience.

    The aim of the project is to develop new tools and methods for statistical modeling of random, heterogeneous, porous material structures. This involves both to work with two- and three-dimensional imaging data of real material structures and with simulation of virtual material structures inspired by real materials to understand the connection between structure and mass transport properties (diffusion and flow). The project constitutes part of a collaboration with several major Swedish industries, as well as with experimentalists in academia, and the methods and software that are developed within the project will be applied to real, industry-relevant materials used in for example hygiene products, packaging materials, pharmaceuticals, etc.

    As part of the VINNExcellence Centre SuMo Biomaterials (SuMo), the project ‘Material structures seen through microscopy and statistics’ funded by the Swedish Foundation for Strategic Research, and the project ‘Mass transport properties of soft porous granular materials’ funded by the Swedish Research Council, many tools for image analysis, statistical characterization, and generation of virtual material structures have been developed. Further, within the SuMo Centre, state-of-the-art software for lattice Boltzmann-based diffusion and flow simulations is available.

    The aim is to build upon the accumulated knowledge from these projects and:

    (1) Develop new image analysis and segmentation algorithms for image data. Highly accurate automatic or semi-automatic image analysis methods are crucial for segmentation of imaging data of material structures. This is particularly important for 3D data where data size makes complete manual segmentation very time-consuming.

    (2) Develop new spatial and spatio-temporal models for fiber materials, foams, granular media, etc, using knowledge acquired through studying real materials.

    (3) Perform simulation studies of mass transport properties using available simulation tools developed in related projects together with new methods of generating material structures. Exploring material structures in the computer is important to avoid costly and time-consuming experimental studies.

    The goal is a deep understanding of the relationships between microstructure and properties that will benefit both further research and applications.

    This position is one of three postdoctoral positions within the new project CoSiMa, which is part of the SuMo Biomaterials centre (www.chalmers.se/sumo).

    References
    S Barman, D Bolin. A three‐dimensional statistical model for imaged microstructures of porous polymer films. Journal of Microscopy, 269, 247-258 (2018).

    H Häbel, T Rajala, C Boissier, M Marucci, K Schladitz, C Redenbach, A Särkkä. A three-dimensional anisotropic point process characterization for pharmaceutical coatings. Spatial Statistics, 22, 306-320 (2017).

    M Röding, K Gaska, R Kádár, N Lorén. Computational screening of diffusive transport in nanoplatelet-filled composites: Use of graphene to enhance polymer barrier properties. ACS Applied Nano Materials, 1, 160−167 (2018).

    M Röding, E Schuster, K Logg, M Lundman, P Bergström, C Hanson, T Gebäck, N Lorén. Computational high-throughput screening of fluid permeability in heterogeneous fiber materials. Soft Matter, 12, 6293-6299 (2016).

    About the division and the department
    At the Division of Applied Mathematics and Statistics we conduct research at a high international level in areas such as biomathematics, bioinformatics, computational mathematics, optimisation, mathematical statistics, kinetic theory. More information about our research groups is available on the website http://www.chalmers.se/en/departments/math/research/research-groups/

    We have an international environment with frequent exchanges with other universities around the world. The department provides a friendly, creative, and supportive atmosphere with a steady flow of international guests. At the division there are many committed teachers with extensive and broad experience of all aspects of higher education. Together with the Divisions of Algebra and Geometry and Analysis and Probability we form the academic part of the department of Mathematical Sciences, which is a joint department of Chalmers and the University of Gothenburg, and one of the largest in mathematics in the Nordic countries with a faculty core of about 80. More information about us is available on the website http://www.chalmers.se/en/departments/math/.

    At the Agrifood and Bioscience unit at RISE Research Institutes of Sweden we conduct research and development in food, agriculture, and bioscience related topics including quantitative microscopy, image analysis, computational materials science, hetereogeneous and viscoelastic materials, microbiology and food processing. The unit comprises about 100 people, and is part of the division of Bioscience and Materials at RISE Research Institutes of Sweden. More information about us is available on the website
    http://www.sp.se/en/units/risebiovet/fb/Sidor/default.aspx

    Major responsibilities
    You are expected to pursue a vigorous research program and collaborate with our researchers.

    During the employment at the Department of Mathematical Sciences, we offer you the possibility that up to 20 % of your work may be spent on teaching. RISE Agrifood and Bioscience does not offer teaching.

    Position summary
    Full-time temporary employment. The position is limited to a maximum of two years (1+1).

    Qualifications
    You should have a Ph.D. in Applied Mathematics, Mathematical Statistics, Computational Science, possibly also Physics, or equivalent, completed before the starting date of employment and not earlier than three years before the application deadline. Fluency in English is expected. Experience in image analysis and processing, machine learning, spatial statistics, and good programming skills is meriting.

    Chalmers continuously strives to be an attractive employer. Equality and diversity are substantial foundations in all activities at Chalmers.

    Our offer to you
    Chalmers offers a cultivating and inspiring working environment in the dynamic city of Gothenburg.
    Read more about working at Chalmers and our benefits for employees.

    Application procedure
    The application should be marked with Ref 20180454 and written in English. The application should be sent electronically and be attached as pdf-files, as below:

    CV: (Please name the document as: CV, Surname, Ref. number) including:
    • CV, include complete list of publications
    • Previous teaching and pedagogical experiences
    • Two references that we can contact.

    Personal letter: (Please name the document as: Personal letter, Family name, Ref. number) including:
    • 1-3 pages where you introduce yourself and present your qualifications.
    • Previous research fields and main research results.
    • Future goals and research focus. Are there any specific projects and research issues you are primarily interested in?

    Other documents:
    • Attested copies of completed education, grades and other certificates.

    Please use the button at the foot of the page to reach the application form. The files may be compressed (zipped).

    Application deadline: 28 September, 2018

    For questions, please contact:
    Deputy head of Division, Stig Larsson, stig[at]chalmers.se
    Aila Särkkä, aila[at]chalmers.se
    Magnus Röding, magnus.roding[at]ri.se

    *** Chalmers declines to consider all offers of further announcement publishing or other types of support for the recruiting process in connection with this position. ***

    Chalmers University of Technology conducts research and education in engineering sciences, architecture, technology-related mathematical sciences, natural and nautical sciences, working in close collaboration with industry and society. The strategy for scientific excellence focuses on our eight Areas of Advance; Building Futures, Energy, Information & Communication Technology, Life Science, Materials Science, Nanoscience & Nanotechnology, Production and Transport. The aim is to make an active contribution to a sustainable future using the basic sciences as a foundation and innovation and entrepreneurship as the central driving forces. Chalmers has around 11,000 students and 3,000 employees. New knowledge and improved technology have characterised Chalmers since its foundation in 1829, completely in accordance with the will of William Chalmers and his motto: Avancez!

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    OFFICIAL WEBPAGE

    October 3rd to October 5th 2018
    Workshop at the Institute Camille Jordan

    Faculté des Sciences et Techniques
    Bâtiment A, salle A 14

    Aim of the workshop

    The aim of the workshop is to bring together researchers at the junction of discrete random structures, in particular random graphs, random walks, and its applications to complex networks.

    Poster of the workshop

    Organizing committee

    • Dieter Mitsche
    • Pascal Grail
    • Pascale Villet

    Speakers

    Julien Barré (Univ. d'Orléans): Rigidity percolation and random graphs
    Oriane Blondel (CNRS, Univ. Lyon 1)
    Jérémie Bouttier (ENS Lyon): Some aspects of random maps coupled with matter systems
    Pierre Calka (Univ. Rouen)
    Philippe Chassaing (Univ. de Lorraine): The impatient collector
    David Coupier (Univ. Valenciennes)
    Josep Diaz (UPC Barcelona): Evolutionary Graph Theory
    Roland Diel (Univ. Nice Côte d'Azur)
    Joachim Giesen (Univ. Jena)
    Emmanuel Jacob (ENS Lyon)
    Lefteris Kirousis (National and Kapodistrian Univ. Athens): The Lovasz Local Lemma: An introduction and some recent results
    Antoine Lejay (INRIA Nancy Grand-Est)
    Grégory Miermont (ENS Lyon)
    Grigory Panasenko (Univ. Saint-Etienne)
    Guillem Perarnau (Univ. Birmingham): Efficient sampling of random colorings
    Vlady Ravelomanana (Univ. Paris 7)
    Christophe Sabot (Univ. Lyon 1)
    Bruno Schapira (Univ. Aix-Marseille)
    Fabio Toninelli (CNRS, Univ. Lyon 1)

    Schedule

    Details will be announced soon.
    Talks start wednesday October 3 after lunch and end Friday October 5 before lunch.

    Registration

    No fee. However, for logistical reasons, registration is mandatory.
    In order to register, please contact us by email: dieter.mitsche(AT)univ-st-etienne.fr, by September 26.

    Practical Information

    The talks will take place in the Faculty of Sciences, room A 14, 23 rue du Docteur Paul Michelon, Saint-Étienne.

    Venue: Address: Institut Camille Jordan, Faculté des Sciences et Techniques, 23 rue du Docteur Paul Michelon, 42023 Saint-Étienne Cedex 2

    How to arrive from Aeroport Lyon Saint-Exupery: The shuttle Ouibus makes the connection form Lyon Saint-Exupery airport to Saint-Etienne main train station.
    Alternative: By Rhône-express (https://www.rhonexpress.fr/) + train (TER Lyon Part Dieu-Saint-Etienne: (https://www.ter.sncf.com/auvergne-rhone-alpes)

    How to arrive from Lyon-Part Dieu or Lyon-Perrache: By train, https://www.ter.sncf.com/auvergne-rhone-alpes (TER journeys to/from Saint-Etienne and Lyon take under 50 minutes).

    How to go from the main station Saint-Etienne Chateaucreux to the Institute Camille Jordan: The city bus company: STAS: http://www.reseau-stas.fr/fr/itineraires/4/JourneyPlanner
    The Bus line M4 takes you from the main train station (Saint-Etienne Châteaucreux) to the faculty of Sciences.

    Accomodation:
    Continental Hôtel ★★
    10 Rue François Gillet - 42000 Saint-Étienne
    Phone: 04.77.32.58.43
    Website: https://www.hotelcontinental42.fr/fr/

    Hôtel du Cheval Noir ★★★
    11 Rue François Gillet - 42000 Saint-Étienne
    Phone: 04.77.33.41.72
    Website: http://www.hotel-chevalnoir.fr/fr

    Access

    Access and Campus Maps

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    The School of Advanced Studies (SAS), University of Tyumen, Russia, is recruiting post-
    docs and professors in biology, ideally specialized in neurobiology but other profiles will be
    considered too. The workload will consist of teaching general undergraduate biology courses
    (about 50% of time), and doing research on the neuroscience of consciousness and free will within
    an interdisciplinary team (e.g. performing Libet-like experiments using EEG, fMRI etc., available
    in Tyumen). If the candidates are not experts in this type of research, they will be given the means
    to become experts; they also have the possibility to devote a (small) part of their research to other
    personal research in biology. After one successful year, the post-docs will be invited to apply for
    a permanent faculty position at the SAS.
    The School of Advanced Studies of the University of Tyumen is a new institution, created
    in 2017 and incentivized by the Russian Federal Government to become a centre of excellence in
    interdisciplinary research and teaching (the students of SAS choose majors in biology, IT,
    humanities and social sciences). The scientists we will recruit will receive western-standard
    salaries and will have a unique opportunity to develop their own research in neurobiology,
    neurophysiology, neuropsychology or related fields, while being offered the possibility to interact
    with other disciplines, notably from humanities and social sciences. For info on the general context
    of the project: https://sas.utmn.ru/en/free-will-en/. For questions, please contact Dr. Louis
    Vervoort at l.vervoort@utmn.ru.
    For interested parties, please submit your application via e-mail to l.vervoort [at] utmn.ru.
    Include a cover letter (explaining the relevance of your application for these positions), a full CV,
    and two letters of recommendation. Applications will be considered from September 1 st 2018
    throughout 2018 until the positions are filled; first applications will be given first attention.

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  • Geo-Sci-Info

    Academic Fellowship positions

    The University of Nottingham is a research intensive university ranked in the top 10 in the UK and in the top 30 of Europe. It has a strong tradition in Cognition, Perception, Mathematical Biology, and structural and functional Imaging. To support further development across these areas, and stimulate new research opportunities, the University has recently made a major investment in Computational Neuroscience.

    As part of this investment we are inviting applications for three independent fellowships (3 years). The positions are ideal for researchers with a strong vision that are planning to develop their own research group in a supportive, interdisciplinary environment with outstanding research facilities.

    We are interested in candidates working across a broad spectrum of research topics areas in Computational Neuroscience, including functional neuroimaging, neural networks, and models of cognition. Research methodologies could include numerical simulation, machine learning, theoretical neuroscience, and innovative data analysis. The Fellows will be based in the School of Psychology and/or Mathematical Sciences.

    For the online application form see
    https://www.nottingham.ac.uk/jobs/currentvacancies/ref/SCI219418

    Informal inquiries:
    Prof Mark van Rossum, mark.vanrossum [at] nottingham.ac.uk, or
    Prof Mark Humphries, lpzmdh [at] exmail.nottingham.ac.uk.

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    Capture du 2018-07-25 06-24-56.png

    OFFICIAL WEBPAGE

    DOWNLOAD PROGRAM

    Conférence-débat de l'Académie des sciences, de 14h30 à 16h45, dans la Grande salle des séances de l’Institut de France - Inscription obligatoire avant le 9 octobre 2018

    Organisateur
    Alain Berthoz, professeur honoraire au Collège de France, membre de l'Académie des sciences et de l’Académie des technologies.

    Intervenants

    • Neil Burgess, professeur et directeur de l’Institute of Cognitive Neuroscience, University College London, membre de la Royal Society et de l’Academy of Medical Sciences
    • Laure Rondi-Reig, directrice de recherche au CNRS à l’Institut de Biologie Paris–Seine, Université Pierre-et-Marie-Curie
    • Alain Berthoz, professeur honoraire au Collège de France, membre de l'Académie des sciences et de l’Académie des technologies
    • Daniel Bennequin, professeur émérite au département de mathématiques de l’université Paris Diderot.

    Résumé
    Le thème général de cette séance sera d’exposer des découvertes et théories récentes concernant le traitement cérébral de l’espace et plus particulièrement la flexibilité des stratégies cognitives et la diversité des réseaux de traitement des divers espaces d’action.
    Neil Burgess décrira les divers types de neurones de la formation hippocampique qui codent la place, la direction, les bords, leur organisation en grilles et les relations avec les objets. Il décrira un modèle mathématique de transformation entre les divers référentiels spatiaux (égocentré, allocentré etc.) et son utilisation pour prédire des symptômes de déficits de la mémoire spatiale dans des pathologies post-traumatiques.
    Laure Rondi-Reig décrira les réseaux neuronaux impliqués dans des activités de navigation spatiale, l’apprentissage et les troubles de la mémoire, dans le cas du vieillissement par exemple. Des données expérimentales sur les mécanismes neurophysiologiques et la diversité des réseaux corticaux et sous corticaux de la cognition spatiale et des modèles inspirés des neurosciences computationnelles seront présentés.
    Alain Berthoz proposera que le cerveau traite avec des réseaux différents et des géométries différentes pour les divers espaces (corps, préhension, locomoteur, environnemental). Il décrira des données récentes sur les bases neurales des changements de perspective, le développement chez l’enfant des fonctions visuo-spatiales et l’intervention de la manipulation des référentiels spatiaux dans l’empathie et son implication en pathologie psychiatrique.
    Daniel Bennequin proposera que pour guider l’adaptation des actions et des perceptions, les cerveaux des animaux et de l’homme mettent en place une variété de géométries, Euclidiennes et non Euclidiennes, et de dynamiques. L’exposé présentera un travail sur une nouvelle sorte de géométrie : un topos d’espaces au-dessus d’une catégorie (site) représentant la préparation et l’exécution d’une classe de mouvements; par exemple la préhension, la locomotion, la navigation, l’imagination.

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    Capture du 2018-07-16 10-08-30.png

    OFFICIAL WEBSITE

    This ICML workshop seeks to broaden the role of geometric models and thinking in machine-learning research.

    The interplay between machine learning and geometry is an active field of research drawing the attention of researchers from many fields as it offers not only beautiful mathematical and statistical theory but also substantial impact on important real-world problems in machine learning. Examples of this interplay include, but are not limited to:

    • geometric insights into deep learning (helping us to better understand and improve these models);
    • statistical models that respect and exploit the constraints of geometric data ("statistics on manifolds");
    • (Riemannian) manifold learning;
    • viewing probability distributions as points on a nonlinear manifold ("information geometry");
    • optimization over nonlinear manifolds;
    • Wasserstein spaces and their applications;
    • understanding the effect of encoding group invariances (e.g. rotation invariance) on the intrinsic geometry of the data space, which becomes a quotient space.

    This ICML workshop is co-located with ICML, IJCAI and AAMAS in Stockholm.

    The Bosch center for AI generously sponsors a prize for the best abstract, which will be presented at the workshop.

    Confirmed Speakers

    Venue

    The workshop takes place at the ICML conference venue, room A4.

    Organizers

    The conference is jointly organized by:

    For matters regarding the conference, you can contact the organizers at gimli.meeting[at]gmail.com.

    We are grateful for funding from the Bosch Center for Artificial Intelligence, the Villum Fonden Young Investigator program and the European Research Council (ERC) through a starting grant.

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    Postdoc and PhD student positions in machine learning, Finnish Center for Artificial Intelligence

    Finnish Center for Artificial Intelligence (FCAI; http://fcai.fi) is looking for exceptional doctoral students and postdoctoral researchers to tackle complex and exciting problems in the field of machine learning. Come and join us to create the Real AI that is data-efficient, trustworthy and understandable!
    FCAI brings together the world-class expertise of Aalto University and the University of Helsinki in AI research, strengthened further with an extensive set of companies and public sector partners, creating an attractive, world-class ICT hub in Helsinki metropolitan area.
    Our research agenda is spearheaded by 5 research programs with multiple research groups involved in each. We are currently hiring doctoral students and postdoctoral researchers in the following FCAI research programs and the detailed projects listed below.

    Research programs:

    1. Agile probabilistic AI. Keywords: Probabilistic programming; Robust and automated Bayesian machine learning
    2. Simulator-based inference: Approximate Bayesian Computation ABC; likelihood-free inference; Generative adversarial networks (GAN); applications in many fields including medicine, materials design, visualization, business, …
    3. Next generation data-efficient deep learning; including deep reinforcement learning
    4. Privacy-preserving and secure AI: Privacy-preserving machine learning; differential privacy; adversarial machine learning
    5. Interactive AI: Interactive machine learning; probabilistic inference of cognitive models from data; probabilistic programming for behavioral sciences

    Specific projects:

    1. Constraint-Based Optimization and Machine Learning
    2. Probabilistic Machine Learning
    3. Probabilistic machine learning for personalized medicine
    4. Probabilistic modeling and machine learning for bioinformatics
    5. Non-parametric probabilistic machine learning
    6. Bioinformatics and computational biology
    7. Computational HCI
    8. Privacy-preserving federated machine learning
    9. Probabilistic user modelling in interactive human-in-the-loop machine learning

    Deadline for doctoral students: August 12, 2018. Postdoc applications received by August 12, 2018 will receive full treatment.

    More details here: http://www.fcai.fi/fcainews/2018/7/2/postdoc-and-doctoral-student-positions-in-machine-learning

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  • Geo-Sci-Info

    1] Research Associate /Senior Research Associate positions, University of Bristol, UK
    [2] Postdoc position in Computational Neuroscience at the CRM, Barcelona, Spain
    [3] 1 research associate and 2 funded PhD positions on the evolution of neural learning and plasticity, Loughborough University, UK
    [4] Two PhD Student Positions in Computer Vision for Learnings Systems at MPI-IS, Tuebingen, Germany
    [5] Postdoctoral Scholars in Computational Brain Science – Brown Institute for Brain Sciences, Providence, RI
    [6] Postdoctoral Research Associate in Neural Engineering, University of Essex, UK
    PhD and Research Fellows Positions in Machine Learning / Deep Learning, Hochreiter Group, Linz - Austria
    [7] 10 PhD Positions in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria
    [8] Five Research Fellows in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria
    [9] Two Research Fellows in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria
    [10] Post doc position in comparative cognition (4-yrs), Vienna, Austria
    [11] PhD call for applications PROPOSAL OF SUBJECT OF THESIS - New Stochastic Optimization Approaches to the Aerospace Vehicle Spatial Design Problem


    [6] De: Luca Citi <lciti[at]essex.ac.uk>

    Postdoctoral Research Associate in Neural Engineering
    University of Essex - School of Computer Science and Electronic Engineering

    The Essex Brain-Computer Interfaces and Neural Engineering laboratory is happy to announce a postdoctoral position in the MURI project "Closed-Loop Multisensory Brain-Computer Interface for Enhanced Decision Accuracy" (more information at: https://sites.usc.edu/muri-project/ ).
    The project is a very exciting one and we have teamed up with outstanding partners from USC, Harvard, UCL, Berkeley, Imperial, among others.

    The Essex team's work on the project focuses on brain-computer interfacing, on algorithms for signal processing and extraction of information from EEG and other physiological signals, on behavioural and neuro-physiological investigations of multisensory feature binding and integration, as well as methods for predicting the level of attention and confidence in decision making of a participant from behavioural, physiological and neural data in real time.
    The duties of the role include conducting research, development and dissemination of neural engineering techniques within the MURI project.

    Applicants are expected to hold a PhD (or be very close to submitting their PhD thesis) in Biomedical Engineering, Brain-computer Interfaces, Neural Engineering, Electronic Engineering, Statistics, Physics, Computer Science or a closely related discipline. The ideal candidate will have significant experience in signal processing, statistical modelling of neural signals and processes. Applicants are also expected to have a strong publication record (relative to their career stage) as first author, ideally including publications in 1st quartile journals in relevant areas.

    The successful applicant will join the Essex team - formed by Dr Luca Citi (PI), Prof Riccardo Poli (Co-I and UK team leader), Dr Caterina Cinel (named Research Fellow) - and will be part of the Essex BCI-NE Lab, today the UK's largest research group in brain-computer interfaces.

    This post is initially fixed-term until the 31st of October 2019 but may be extended for two more years if further funding is approved.

    Appointment will be made as Senior Research Officer.

    Closes: 30th June 2018
    Job Ref: REQ01468
    Salary: £32,548 - £34,521 per annum

    Further information and application instructions:
    https://www.jobs.ac.uk/job/BKF396


    [7] De : Ulrich Bodenhofer <bodenhofer[at]bioinf.jku.at>

    10 PhD Positions in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria

    Johannes Kepler University Linz (JKU), Austria, is looking for research assistants and junior researchers in the area of machine learning and deep learning with Sepp Hochreiter. These fully-funded positions will be affiliated with the LIT AI Lab and the Institute for Machine Learning andCandidates are expected to enroll into JKU’s PhD programme, and have to option to complete their PhD under Sepp Hochreiter’s supervision.

    Job description:
    • conduct research in machine learning / deep learning with the aim to obtain a PhD within four years,
    • publish in renowned international journals and conferences,
    • work in research projects at the LIT AI Lab or in collaboration with partners.

    Requirements:
    • MSc degree or equivalent,
    • strong interest and previous education in machine learning (e.g. deep learning, reinforcement learning, kernel methods, probabilistic modeling, meta-learning, attention models); a track record in the field is a plus (e.g. MSc thesis, publications, project experience),
    • knowledge in one or more of the following application domains is a plus: signal processing, vision, speech, natural language processing, physics, bio-/chemoinformatics, computational medicine, autonomous driving.

    About the group: Within the last years, Sepp Hochreiter (who is best known for the invention of LSTM, for the Vanishing Gradient problem, “Flat Minima”, and “Learning to Learn”) has built up a dynamic team of more than20 researchers. The group has recently achieved widely acclaimed contributions and successes, such as, winning the NIH’s Tox21 toxicity prediction challenge with deep learning, the invention of the ELU activation function, Self-Normalizing Networks, and providing a convergence proof for GAN learning. The group has many international collaborations and receives funding from national and international research programs (like the EU) as well as from companies, such as, Johnson&Johnson, Merck, Bayer, Zalando and from joined labs like the Audi.JKU Deep Learning Center.

    About the location: The area offers an excellent quality of living in the heart of Europe – close to the alps between Vienna, Salzburg, Prague and Munich. Linz provides a superb cultural environment, most famous for the Ars Electronica Festival, the Brucknerfest, and the nearby Salzburg Festival. The picturesque and versatile landscape provides countless options for recreation and sports in nature (skiing, hiking, climbing, cycling, and many more).

    If you have questions, please contact: Prof. Dr. Sepp Hochreiter, +43 732 2468 4520, recruitment[at]bioinf.jku.at.


    [8] De : Ulrich Bodenhofer <bodenhofer[at]bioinf.jku.at>

    Five Research Fellows in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria

    Johannes Kepler University Linz (JKU), Austria, is looking for five post-doctoral research fellows to advance machine learning and deep learning in close collaboration with Sepp Hochreiter. These positions are affiliated with the LIT AI Lab and the Institute for Machine Learning.

    Job description:
    • conduct independent research in the field,
    • collaborate in machine learning and deep learning projects,
    • publish in renowned international journals and conferences.

    Requirements:
    • PhD degree,
    • track record in machine learning (e.g. deep learning, reinforcement learning, kernel methods, probabilistic modeling, meta-learning, attention models),
    • knowledge in one or more of the following application domains is a plus: signal processing, vision, speech, natural language processing, physics, bio-/chemoinformatics, computational medicine, autonomous driving,
    • willingness and ability to work in a team.

    About the group: Within the last years, Sepp Hochreiter (who is best known for the invention of LSTM, for the Vanishing Gradient problem, “Flat Minima”, and “Learning to Learn”) has built up a dynamic team of more than20 researchers. The group has recently achieved widely acclaimed contributions and successes, such as, winning the NIH’s Tox21 toxicity prediction challenge with deep learning, the invention of the ELU activation function, Self-Normalizing Networks, and a convergence proof for GAN learning. The group has many international collaborations and receives funding from national and international research programsas well as from companies, such as, Johnson&Johnson, Merck, Bayer, Zalando and from joined labs like the Audi.JKU Deep Learning Center.

    About the location: The area offers an excellent quality of living in the heart of Europe – close to the alps between Vienna, Salzburg, Prague and Munich. Linz provides a superb cultural environment, most famous for the Ars Electronica Festival, the Brucknerfest, and the nearby Salzburg Festival. The picturesque and versatile landscape provides countless options for recreation and sports in nature (skiing, hiking, climbing, cycling, and many more).

    If you have questions, please contact: Prof. Dr. Sepp Hochreiter, +43 732 2468 4520, recruitment[at]bioinf.jku.at.


    [9] De : Ulrich Bodenhofer <bodenhofer[at]bioinf.jku.at>

    Two Research Fellows in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria

    Johannes Kepler University Linz (JKU), Austria, is looking for two post-doctoral research fellows to advance machine learning and deep learning research with Sepp Hochreiter. These six year positions are affiliated both with the newly established LIT AI Lab and the Institute for Machine Learning.

    Job description:
    • conduct independent research in the field,
    • collaborate in machine learning and deep learning projects,
    • publish in renowned international journals and conferences,
    • supervise students; prepare and hold lectures; support study programs.

    Requirements:
    • PhD degree,
    • track record in machine learning (e.g. deep learning, reinforcement learning, kernel methods, probabilistic modeling, meta-learning, attention models),
    • knowledge in one or more of the following application domains is a plus: signal processing, vision, speech, natural language processing, physics, bio-/chemoinformatics, computational medicine, autonomous driving,
    • willingness and ability to work in a team and to support students and junior researchers.

    About the group: Within the last years, Sepp Hochreiter (who is best known for the invention of LSTM, for the Vanishing Gradient problem, “Flat Minima”, and “Learning to Learn”) has built up a dynamic team of more than 20 researchers. The group has recently achieved widely acclaimed contributions and successes, such as, winning the NIH’s Tox21 toxicity prediction challenge with deep learning, the invention of the ELU activation function, Self-Normalizing Networks, and providing a convergence proof for GAN learning. The group has many international collaborations and receives funding from national and international research programs as well as from companies, such as, Johnson&Johnson, Merck, Bayer, Zalando and from joined labs like the Audi.JKU Deep Learning Center.

    About the location: The area offers an excellent quality of living in the heart of Europe – close to the alps between Vienna, Salzburg, Prague and Munich. Linz provides a superb cultural environment, most famous for the Ars Electronica Festival, the Brucknerfest, and the nearby Salzburg Festival. The picturesque and versatile landscape provides countless options for recreation and sports in nature (skiing, hiking, climbing, cycling, and many more).

    If you have questions, please contact: Prof. Dr. Sepp Hochreiter, +43 732 2468 4520, recruitment[at]bioinf.jku.at.

    Prospective applicants interested in these positions are requested to electronically send an application via the online portal http://jku.at/application. Please include “Job Reference Number 3619” (deadline: July 30, 2018) or “Job Reference Number 3578” (deadline: July 4, 2018).


    [10] De: Isabelle Charrier <isabelle.charrier[at]u-psud.fr>

    Post doc position in comparative cognition (4-yrs), Vienna, Austria

    At the Unit of Comparative Cognition, Messerli Research Institute, Vienna, Austria, we are seeking a postdoctoral researcher who is eager to investigate cognitive and emotional processes in non-human animals, especially dogs. At the Clever Dog Lab we are committed to researching the behavioural, physiological (including neuronal) and genetic underpinnings of dog cognition. The successful candidate will have the opportunity to develop her/his own research agenda, using a large repertoire of state-of-the-art techniques and methodologies (including fMRI, eye-tracking, touch screens, automatized video analysis and behaviour annotation) and benefiting from administrative and technical support from members of the unit (including a lab manager, mechanical and electronic technicians and IT personnel).

    Please see the full advertisement here:
    http://www.vetmeduni.ac.at/fileadmin/v/z/mitteilungsblatt/stellen/2017_2018/20180615_Postdoc_Comparative_Cognition.pdf

    More details about the unit of Comparative Cognition, Vienna:http://www.vetmeduni.ac.at/en/messerli/science/cognition

    Informal enquiries about this post may be directed to Professor Ludwig Huber, ludwig.huber[at]vetmeduni.ac.at

    Application deadline: 8th July 2018.

    --
    Prof. Ludwig Huber, PhD
    Head of Comparative Cognition
    Messerli Research Institute

    University of Veterinary Medicine, Vienna (Vetmeduni Vienna)
    Veterinaerplatz 1, 1210 Vienna, Austria
    T +43 1 25077-2680
    M +43 664 60257-6250
    ludwig.huber[at]vetmeduni.ac.at
    www.vetmeduni.ac.at/messerli

    Partner institutions of the Messerli Research Institute:
    Messerli-Foundation, University of Veterinary Medicine Vienna, Medical University of Vienna, University of Vienna


    [11] De : Rachid Chelouah <rc[at]eisti.eu>

    PhD call for applications PROPOSAL OF SUBJECT OF THESIS - Reference: TIS-DTIS-2018-15

    Title: New Stochastic Optimization Approaches to the Aerospace Vehicle Spatial Design Problem

    The host laboratory at ONERA : Domain : TIS
    Department: Information Processing and Systems Department - Unit: Design and Evaluation of Aerospace Vehicles
    Location (ONERA center): Palaiseau

    ONERA responsibles: Tel. 01 80 38 66 30 Email :
    Romain Wuilbercq romain.wuilbercq[at]onera.fr
    Karim Dahia karim.dahia[at]onera.fr
    Arnault Tremolet arnault.tremolet[at]onera.fr

    The host laboratory at Paris-Seine : Quartz
    Thesis supervisor: Rachid Chelouah Email : rachid.chelouah[at]eisti.eu Tel. :+33 (0)1 34 25 84 20

    Co-encadrant: Stefan Borhofen Email : stefan.bornhofen[at]eisti.eu
    Address: University of Paris-Seine/EISTI, 95011 Cergy-Pontoise Cedex

    Description of the subject
    The pre-project phase for the development of an aerospace vehicle is one that is likely to bring out innovative configurations. In a general way, after a parallel evaluation of several topologies, a particular configuration is chosen on the basis of selection criteria resulting from the optimization of a mission. At present no systematization of the process seems to be possible without having a function of planning in the sense of geometric placement of objects. The problem of the geometric arrangement is defined by the ability to place different objects without interpenetration in an envelope. The generalization of the problem consists in taking into account their functional aspect which can contribute to prohibit or force their relative placement.

    The work focuses on objects and an external topology of 3D vehicle. To solve the problem of geometrical arrangement, two large families stand out, one of excluding all solutions with interpenetrations which supposes the entire evaluated, one speaks of legal placement, the other by authorizing them but by affecting a penalty function according to the degree of interpenetration, this is called relaxed placement. The latter approach, adopted as part of the proposed work, was exploited in [1] highlighting the interest of the coupling between robust multi-objective optimization techniques and a separation method.
    The first part of the work consists in appropriating methods of modeling and rapid evaluation of collisions between objects. Given the chosen relaxed placement, an assessment of this collision should be made by estimating the interpenetration depth, for example. The physical aspects (eg electromagnetic field, radiative radiation) and functional aspects can also be introduced by the notion of region of influence [2]. The consideration of spatial collisions and regions of influence in a planning process will be explored during this thesis.

    Such a problem of placement of objects is characterized by a strong combinatorics. The result is the need to explore a vast space of solutions. The number of variables associated with the quantity of objects to be placed is also important. Finally, the integration of constraints, to be defined exhaustively, is the heart of the problem because of their diverse nature (geometric, functional, thermal, ... etc). Stochastic optimization algorithms therefore appear relevant in order to explore a vast space of solutions, containing many constraints and many variables (complete Np problem) [3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13]. The challenge of this thesis will be to adapt optimization algorithms to the specificity of the problem by introducing for example an algorithmic overlay so as to deal with all constraints.

    The developments of this thesis will be carried out in a capitalization environment such as the ACADIA platform developed at ONERA. In particular, they will be applied to multidisciplinary optimization problems using the OpenMDAO framework (Python, Cython, C ++). The methods and algorithms developed will be applied to various aerospace applications or road and rail transport that will verify their genericity.

    References :
    [1] G. Jacquenot, « Méthode générique pour l’optimisation d’agencement géométrique et fonctionnel », 18 Janvier 2010.
    [2] Joaquim P. L. Viegas, Susana M. Vieira, Joao M. C. Sousa and, Elsa M. P. Henriques, « Metaheuristics for the 3D Bin Packing Problem in HAPE3D the Steel Industry », 2014 IEEE Congress on Evolutionary Computation (CEC) July 6-11, 2014, Beijing, China.
    [3] Xiao LIU, Jia-min LIU, An-xi CAO, Zhuang-le YAO, « - a new constructive algorithm for the 3D irregular packing problem », Front Inform Technol Electron Eng 16(5):380-390, 2015
    [4] Marouene Kefi, Paul Richard, Thuong Hoang, Takehiko Yamaguchi and Vincent Barichard, « Using Constraint Solver for 3D Layout Assistance in Human-scale Virtual Environment », HUCAPP 2017 - International Conference on Human Computer Interaction Theory and Applications, 2017.
    [5] Giorgio Fasano, « A global optimization point of view to handle non-standard object packing problems », J Glob Optim, 2012.
    [6] C. Monjaret, « Introduction aux méthodes d’optimisation pour l’aménagement spatial », RT 1/14706 DPRS, 2010.
    [7] C. Leboucher, R. Chelouah, H.S. Shin, S. Le Ménec, P. Siarry, A. Tsourdos and A. Kotenkoff, An Enhanced Particle Swarm Optimisation Method Integrated With Evolutionary Game Theory, IEEE Transactions on Computational Intelligence and AI in Games, Janv 2018
    [8] Peio Loubiere, Astrid Jourdan, Patrick Siarry and Rachid Chelouah, A modified sensitivity analysis method for driving a multidimensional search in the Artificial Bee Colony algorithm, IEEE Congress on Evolutionary Computation, IEEE CEC 2016,
    [9] C. Leboucher, H.S. Shin, P. Siarry, S. Le Ménec, R. Chelouah, and A. Tsourdos, Convergence Proof of an Enhanced Particle Swarm Optimisation Method Integrated with Evolutionary Game Theory, Information Sciences, ScienceDirect, Elsevier, DOI doi:10.1016/j.ins.2016.01.011, pp. 389-411 2016:
    [10] James Kennedy, Rachid Chelouah, Maurice Clerc et Patrick Siarry, Swarm Intelligence Research édité en deux tomes par IGI Publishing ISSN 1947-9263 et ISSN 1947-9271, IJSIR, 2012.

    Profile of candidate du (de la) candidat (e) :
    Education: Student in Master 2 or engineering school
    Desired Specificities: Mathematical Modeling, Stochastic Optimization, Artificial Intelligence, Computer Science.

    Person to contact :
    Rachid Chelouah
    Laboratoire Quartz
    Université Paris-Seine / EISTI
    Mail : rc[at]eisti.eu

    Karim Dahia
    ONERA
    Laboratoire : Conception et Évaluation de Véhicules Aérospatiaux
    mail : karim.dahia[at]onera.fr


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  • Geo-Sci-Info

    PhD psotitions : 6 offers

    [1] Research Associate /Senior Research Associate positions, University of Bristol, UK
    [2] Postdoc position in Computational Neuroscience at the CRM, Barcelona, Spain
    [3] 1 research associate and 2 funded PhD positions on the evolution of neural learning and plasticity, Loughborough University, UK
    [4] Two PhD Student Positions in Computer Vision for Learnings Systems at MPI-IS, Tuebingen, Germany
    [5] Postdoctoral Scholars in Computational Brain Science – Brown Institute for Brain Sciences, Providence, RI
    [6] Postdoctoral Research Associate in Neural Engineering, University of Essex, UK
    PhD and Research Fellows Positions in Machine Learning / Deep Learning, Hochreiter Group, Linz - Austria
    [7] 10 PhD Positions in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria
    [8] Five Research Fellows in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria
    [9] Two Research Fellows in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria
    [10] Post doc position in comparative cognition (4-yrs), Vienna, Austria
    [11] PhD call for applications PROPOSAL OF SUBJECT OF THESIS - New Stochastic Optimization Approaches to the Aerospace Vehicle Spatial Design Problem


    [1] De : Jeffrey Bowers <J.Bowers[at]bristol.ac.uk>

    Research Associate /Senior Research Associate positions, University of Bristol
    Salary: £32,548 to £41,212 per annum
    Deadline: 20th August 2018

    We are seeking to appoint talented computational modelers to join an ERC-funded project entitled “Generalisation in Mind and Machine”.  The project explores how well neural networks support human-like generalisation across a range of tasks.  Two postdoctoral fellows and two PhD students are working on project, and we are looking to hire two more persons with PhDs (or potentially BSc) in computer science or cognitive psychology (or a related field) with experience working with neural networks.

    In contrast with the large community of researchers focused on improving the performance of deep networks for applied reasons, the goal of this project is to explore what neural networks tell us about how the brain works.  More specifically, the team will be working on questions of generalisation in the domains of object identification, word identification, short-term memory, and games, amongst other areas.  A core issue is whether networks need to be include symbolic computations to succeed on some forms of generalisation. For overview of some of the issues we are exploring in the project see: Bowers, J. S. (2017). Parallel Distributed Processing Theory in the Age of Deep Networks. Trends in Cognitive Sciences, 12, 950-961.  For more details regarding the project see our ERC website:http://mindandmachine.blogs.bristol.ac.uk/

    This post is available from Oct 1st 2018 (but somewhat flexible about start date) and currently has funding secured until Oct 1st 2021.  To apply go to: https://www.jobs.ac.uk/job/BKR397/erc-research-associate-senior-research-associate/  If you have any questions, please get in touch with Jeff Bowers at: j.bowers[at]bristol.ac.uk

    Jeff Bowers


    [2]  De : Klaus Wimmer <kwimmer[at]crm.cat>

    A fully funded POSTDOC POSITION in computational neuroscience is available in the lab of Klaus Wimmer, Computational Neuroscience Group at the CRM, Barcelona, Spain.

    Profile
    We are looking for an enthusiastic and scientifically curious researcher with a strong interest in computational neuroscience. The perfect candidate has a strong mathematical, physical or engineering background, scientific programming skills (Matlab, Python), and a keen interest in biological neural systems. Knowledge in computational neuroscience, dynamical systems, machine learning or advanced statistics is a plus. Good team spirit is a must.

    Research topic
    The neural basis of decision making and working memory has been studied extensively, yet our understanding of how distributed circuits in the brain perform these cognitive functions is only at the beginning. Models of cortical circuits can shed light on the underlying neural network dynamics. The Postdoc will work on building such models and on cutting-edge analysis of large-scale neural activity recordings (neuronal population recordings, fMRI, EEG).

    The lab
    The Computational Neuroscience Group is based at the Centre de Recerca Matemàtica at the campus of the Universitat Autònoma de Barcelona. It is a joint effort of Alex Roxin and Klaus Wimmer, and forms part of a larger network of theoretical and systems neuroscience labs in Barcelona. The successful candidate will benefit from a vibrant and stimulating research community and will have the opportunity of enjoying a lively city.

    More information can be found at: https://sites.google.com/view/wimmerlab

    How to apply
    Interested candidates should e-mail their application as a single pdf file to Klaus Wimmer, kwimmer[at]crm.cat, with the subject “Postdoc 2018”. The application should include: (1) CV with publication list, (2) a brief description of research experience and interests, (3) contact information for two references.
    The position is available immediately and applications will be accepted until it is filled. Informal inquiries are welcome.

    -- 
    Klaus Wimmer
    Ramón y Cajal researcher at
    Centre de Recerca Matemàtica
    Campus de Bellaterra, Edifici C
    08193 Bellaterra (Barcelona)
    Spain
    Tel. +34 935 86 85 15
    https://sites.google.com/view/wimmerlab


    [3] De : Andrea Soltoggio <A.Soltoggio[at]lboro.ac.uk>

    1 research associate and 2 funded PhD. postions on the evolution of neural learning and plasticity

    One research associate and two funded Ph.D. positions are available at the Computer Science Department, School of Science, Loughborough University, UK, on the topics of the evolution of lifelong learning in neural networks.

    Research.  The aim is to develop new neuroevolution algorithms for lifelong learning. The objectives are to devise machine learning systems that autonomously adapt to changing conditions such as variation of the data distribution, variation of the problem domain or parameters, with minimal human intervention. The approach will use neuroevolution, neuromodulation, and other methodologies to continuously discover and update learning strategies, implement selective plasticity, and achieve continual learning.
    For an overview of the research direction, see the paper: Born to Learn: the Inspiration, Progress and Future of Evolved Plastic Artificial Neural Networkshttps://www.researchgate.net/publication/315710249_Born_to_Learn_the_Inspiration_Progress_and_Future_of_Evolved_Plastic_Artificial_Neural_Networks
    Application areas include a variety of automation and machine learning problems, e.g. vision, control, and robotics, with a particular focus on resilience and autonomy.

    Working environment. The research associate and Ph.D. students, based at the Computer Science Department, will work in an international team with opportunities for collaboration and travel. They will have access to a number of robotic platforms such as mobile and flying robots, manufacturing robots, High Performance Computing clusters, and GPU computing. The Computer Science Department and robotics laboratories have ongoing collaborations with large industries and programs to promote start-ups. 
    Loughborough University is ranked 7th in the UK in the 2019 League Table Ranking http://www.thecompleteuniversityguide.co.uk/loughborough/performance ), and is located in Loughborough, a town well connected to London by a 1h20m journey by train.

    Requirements.
    Postdoc: A Ph.D. in Computer Science or related with a strong publication record, coding abilities, predisposition to work in a team and independence, passion for science, solid work ethics.  
    Ph.D. students: The ideal candidate holds (or is about to obtain) a first-class honour undergraduate/postgraduate degree (or equivalent) in Computer Science, Mathematics, Statistics, Electrical or Electronic Engineering, or has authored publications in recognised conferences/journals. Independent working skills are valued as well as the capability of working in a team. Collegiality and interpersonal skills are essential. 
    Excellent English language skills are also essential (see requirements herehttp://www.lboro.ac.uk/international/englang/index.htm)

    Period and salaries. 
    Postdoc position: until June 2020 (with possible extension) with a competitive salary at Grade 6 (http://www.lboro.ac.uk/services/hr/benefits/pay-rewards/)
    Start: as soon as possible. 
    Ph.D. studentships:
    Scholarship: £14,777 per annum plus tuition fees at the UK/EU rate. 
    Start: August 2018 or shortly after.
    Duration: 3.5 years.

    Enquiries and applications. Interested candidates are invited to send preliminary enquiries to a.soltoggio[at]lboro.ac.ukincluding a CV, a university transcript of marks, a list of references, and a statement of about 300 words motivating their interest in this area of research.

    -- 
    Dr.  Andrea Soltoggio
    Lecturer in Artificial Intelligence

    Department of Computer Science,
    Centre for Data Science, 
    Centre for Information Management 
    Haslegrave Building, N.2.03
    Loughborough University
    LE11 3TU, UK

    Phone: +44 (0) 1509 635748
    Email: a.soltoggio[at]lboro.ac.uk
    Twitter: [at]asoltoggio
    Web: http://www.lboro.ac.uk/departments/compsci/staff/dr-andrea-soltoggio.html


    [4] De : Joerg Stueckler <joerg.stueckler[at]tuebingen.mpg.de>

    Two PhD Student Positions in Computer Vision for Learnings Systems 
    within the Embodied Vision Group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany 
    https://ev.is.mpg.de/jobs/2-phd-student-positions-in-computer-vision-for-learning-systems

    Research questions 
    The Embodied Vision Group at the Max Planck Institute for Intelligent Systems investigates novel methods for autonomous systems to learn dynamic scene understanding and to use this understanding to perform complex tasks such as navigation or object manipulation. We aim at systems that learn from raw sensor measurements like images or tactile information and through action within their environment. A research focus in this context is on computer vision topics, including

    • Physical and 3D dynamic scene understanding
    • Learning of predictive environment models
    • Self-supervised and online visual and multi-modal learning
    • Vision-based interactive perception and learning for object manipulation
    • Vision-based navigation for drones and mobile robots
    • Deep reinforcement learning

    The positions 
    We are looking for two PhD students who are holding an outstanding Master’s degree in the computer or natural sciences, electrical or control engineering or applied mathematics. The PhD students will conduct research in one or several of the above topic areas.

    • Candidates should have studied areas related to computer vision and machine learning.Areas of particular interest for us at the moment are deep learning, visual scene understanding, visual/visual-inertial simultaneous localization and mapping, 3D scene reconstruction, robot vision, robot learning and deep reinforcement learning.
    • Successful candidates will typically have ranked at or near the top of their classes and be highly proficient in written and spoken English.
    • Excellent computer science skills as well as a strong mathematical background are required.
    • Prior research experience in computer vision, deep learning, robotic object manipulation or autonomous navigation is a plus.

    The PhD students will receive a PhD funding contract with an initial duration of 3 years. The position is funded for 3-4 years. Salary will be based on previous experience according to guidelines of the German Collective Wage Agreement for the Public Service (TVöD). The earliest start date is August 1st, 2018.

    The Max-Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals. The Max-Planck Society seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply.

    The group 
    The Embodied Vision Group (https://ev.is.mpg.de) is a newly established research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany, and is lead by Dr. Joerg Stueckler. The institute is a world-class center for foundational research in machine learning, computer vision, robotics and material science. Tübingen is a scenic medieval university town, cradled in what is simultaneously one of Germany’s most beautiful landscapes in one of Europe’s most economically successful areas. The working language at the institute is English.

    How to apply 
    Applications and inquiries should be sent quoting reference number 33.18 to Dr. Joerg Stueckler (see contact details below). Applications must be submitted by email as a single pdf (max. 10 MB) and include a CV, motivation letter with research statement, publication list, transcripts of BSc and MSc degrees, and contact details of 2-3 references. Optionally, up to 2 selected own publications or theses can be included in a second pdf (max. 5 MB). Applications should also indicate earliest date of availability.

    There is no fixed application deadline; applications are considered until the positions have been filled or are no longer needed. Preference will be given to applications received before July 15th, 2018.

    For further details on the positions and how to apply, please visit 
    https://ev.is.mpg.de/jobs/2-phd-student-positions-in-computer-vision-for-learning-systems

    Please liberally forward this announcement and share to possibly interested candidates or persons who might know suitable candidates.

    Best regards, 
    Joerg Stueckler

    -- 
    Dr. Joerg Stueckler 
    Max Planck Research Group Leader

    Embodied Vision Group 
    Max-Planck-Institute for Intelligent Systems

    Max-Planck-Ring 4 
    72076 Tübingen 
    Tel. +49 (0)7071 601-385 
    Email: joerg.stueckler[at]tue.mpg.de 
    http://ev.is.mpg.de


    [5] De: Thomas Serre <thomas_serre[at]brown.edu>

    The Frank and Serre labs at Brown university are seeking applicants for the Paul J. Salem Postdoctoral Scholarships in Brain Science. The postdoctoral fellow will lead an exciting new project at the interface between machine learning and neuroscience. In particular, we are looking for computational neuroscience and machine learning experts interested in the intersection between vision, memory and reinforcement learning. Relevant projects in the two groups can be seen in the following example works:

    • Franklin, N.T. & Frank, M.J. (2018). Compositional clustering in task structure learning. PLOS Computational Biology, 14(4): e1006116. http://ski.clps.brown.edu/papers/FranklinFrank_Compositional18.pdf
    • Nassar, M.R., Helmers, J. & Frank, M.J. (2018). Chunking as a rational strategy for lossy data compression in visual working memory. Psychological Review. http://ski.clps.brown.edu/papers/NassarHelmersFrank_chunking.pdf
    • Drew Linsley, Junkyung Kim, Vijay Veerabadran, Thomas Serre.  Learning long-range spatial dependencies with horizontal gated-recurrent units. 2018 https://arxiv.org/abs/1805.08315v1
    • Drew Linsley, Dan Scheibler, Sven Eberhardt, Thomas Serre. Global-and-local attention networks for visual recognition. 2018 https://arxiv.org/abs/1805.08819v1
    Candidates are expected to have a solid background in one or more of the following domains: modern machine learning, computational models of neural dynamics underlying perceptual or cognitive processes, signal processing. In addition, to conducting primary research with neural networks, candidates will be involved in the mentoring of students, and will participate in workshops and challenges at the interface between machine learning and neuroscience (see e.g.,  http://compneuro.clps.brown.edu/datathon_2017/and http://compneuro.clps.brown.edu/2018-modeling-competition/).

    The initial appointment is for 12 months, renewable for another year, and potentially longer depending on funding. The start date is negotiable though an early start is strongly preferred. Salary is commensurate with experience and is competitive. We encourage Salem Scholars to seek external funding during their appointment, as a critical component in their professional development.

    Requirements:
    Candidates must have received their PhDs within 3 years of the application deadline, and will work under the supervision of Drs Frank and Serre who are affiliated with the Carney Initiative for Computation in Brain and Mind.   They must have a strong background in computational neuroscience and machine learning, with a track record of relevant publications at top venues (such as NIPS, ICML, PLOS Computational Biology, etc). Excellent programming skills are required (e.g., C/C++/Matlab/Python/R).

    Application:
    Please send your applications by email to michael_frank[at]brown.edu thomas_serre[at]brown.edu. Please include a brief statement of interests, a curriculum vita, a list of publications and the name of 2-3 reference writers (no letter needed at this stage). There is no deadline for the application but applicants are encouraged to apply as soon as possible as the position will be filled as soon as a suitable applicant is found.

    The Carney Initiative for Computation in Brain and Mind (CICBM; http://compneuro.clps.brown.edu), which began Fall 2013 as a component of BIBS, is an energetic and enthusiastic effort that fosters synergistic collaborations across departments. Groups affiliated with the initiative work on two core levels of computation. The first level focuses on theoretical neuroscience, including computational perception, control over action and learning, and fundamental questions in neuronal networks (synaptic plasticity, circuits, networks, oscillations). The second level focuses on applications and neurotechnology, including brain-machine interfaces, advanced neural data analysis, computer vision, computational psychiatry, and robotics. CICBM has 16 core computational faculty (http://compneuro.clps.brown.edu/people/) spanning six departments, and many more faculty who incorporate computation for theory development, analysis, or both. Computational neuroscience tools at Brown have been applied in projects including brain-machine control of robotic arms in paralyzed humans; models of visual systems in biological organisms and their innovative application for classifying animal behavioral patterns; predicting and quantifying effects of genetics, disease, medications, and brain stimulation on motor and cognitive function; identification of the source of neural rhythms and their roles in sensorimotor function; development of fundamental theories of brain plasticity, and learning; state-of-the art models of machine learning and reinforcement learning in computer science.

    The Carney Institute for Brain Science at Brown University advances multidisciplinary research, technology development, and training in the brain sciences and works to establish Brown University as an internationally recognized leader in brain research. The institute was just endowed with a new $100 million gift. CIBS unites more than 100 faculty from a diverse group of departments at Brown, spanning basic and clinical departments, and physical and biological sciences. CIBS provides a mechanism to advance interdisciplinary research efforts among this broad group.  CIBS provides  essential support to obtain and administer multi-investigator grants for research, infrastructure, and training. The Institute actively seeks new training funds to support interdisciplinary education that transcends that available in individual academic departments.

    -- 
    Thomas Serre | GMT -5:00  EDT | T: +1 401-484-0750 
    Associate Professor of Cog Ling & Psych Sciences | Brown University
    URL: goo.gl/G69SaF | Google Talk: tserre | Skype: thomas.serre

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    Capture du 2018-06-26 12-48-30.png

    OFFICIAL WEBPAGE - INFORMATIONS - DOWNLOADS

    Capture du 2018-06-26 12-51-03.png

    Contact us by email at admin [at] ramp.studio if you are interested in using the platform in your classroom, as an internal tool for prototyping in your data science team, or to launch a data challenge. Consider joining our slack team if you would like to be part of the growing community of rampers.

    Paris-Saclay Center of Data Science: RAMPs are organized by the Paris Saclay Center for Data Science: A multi-disciplinary initiative to define, structure, and manage the data science ecosystem at the University Paris-Saclay.

    What is a RAMP?

    A RAMP is a collaborative data challenge. See here for more details.

    Bibliography:

    • The RAMP framework: from reproducibility to transparency in the design and optimization of scientific workflows, Kégl, Boucaud, Cherti, Kazakçı, Gramfort, Lemaitre, Van den Bossche, Benbouzid, Marini PREPRINT

    Team

    Alumni

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    OFFICIAL WEBSITE - PROGRAM

    POSTER

    The main goal of this program is to gather the community of researchers working on questions that relate in some way statistical physics and high dimensional statistical inference. The format will be several (~10) 3h introductory lectures, and about twice as many contributed invited talks. The topics include:

    • Energy/loss landscapes in disordered systems, machine learning and inference problems
    • Computational and statistical thresholds and trade-offs
    • Theory of artificial multilayer neural networks
    • Rigorous approaches to spin glasses and related models of statistical inference
    • Parallels between optimisation algorithms and dynamics in physics
    • Vindicating the replica and cavity method rigorously
    • Current trends in variational Bayes inference
    • Message passing algorithms
    • Applications on machine learning in condensed matter physics
    • Information processing in biological systems

    Deadline Registration : February 28, 2018

    Lecturers

    • Gerard Ben Arous (Courant Institute)
    • Giulio Biroli (CEA Saclay, France)
    • Nicolas Brunel (Duke University)
    • Yann LeCun (Courant Institute and Facebook)
    • Michael Jordan (UC Berkeley)
    • Stephane Mallat (ENS et college de France)
    • Andrea Montanari (Stanford)
    • Dmitry Panchenko (University of Toronto, Canada)
    • Sundeep Rangan (New York University)
    • Riccardo Zecchina (Politecnico Turin, Italy)

    Speakers

    • Antonio C Auffinger (Northwestern University, USA)
    • Afonso Bandeira (Courant Institute, USA)
    • Jean Barbier (Queens Mary, UK)
    • Quentin Berthet (Cambridge UK)
    • Jean-Philippe Bouchaud (CFM Paris, France)
    • Silvio Franz (Paris-Orsay, France)
    • Surya Ganguli (Stanford, USA)
    • Alice Guionnet (ENS Lyon, France)
    • Aukosh Jagganath (Harvard, USA)
    • Yoshiyuki Kabashima (Tokyo Tech, Japan)
    • Christina Lee (Microsoft Research, USA)
    • Marc Lelarge (ENS Paris, France)
    • Marc Mezard (ENS Paris, France)
    • Leo Miolane (ENS Paris, France)
    • Remi Monasson (ENS Paris, France)
    • Giorgio Parisi (Roma La Sapienza, Italy)
    • Will Perkins (University of Birmingham, UK)
    • Federico Ricci-Tersenghi (Roma La Sapienza, Italy)
    • Cythia Rush (Columbia, USA)
    • Levent Sagun (CEA Saclay, France)
    • Samuel S. Schoenholz (Google Brain, USA)
    • David Jason Schwab (CUNY, USA)
    • Guilhem Semerjian (ENS Paris, France)
    • Alexandre Tkatchenko (University of Luxembourg)
    • Naftali Tishby (Hebrew University, Israel)
    • Pierfrancesco Urbani (CEA Saclay, France)
    • Francesco Zamponi (ENS Paris, France)

    Organizing Committee

    • Florent Krzakala (ENS & UPMC, Paris)
    • Lenka Zdeborova (CEA & CNRS, Saclay)

    Capture du 2018-06-26 11-59-19.png

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    OFFICIAL WEBSITE - REGISTRATION

    Presentation

    This Summer School will consist in two courses given by professors Sergey Bobkov (Minneapolis) and Mokshay Madiman (Delaware) on Information Theory and Convex Analysis. The aim is to bring researchers from different communities (Probability, Analysis, Computer science) in the same place.
    Participation of postdocs and PhD students is strongly encouraged. The school has some (limited number of) grants for young people (see the "registration" link above).

    Abstracts        
    Sergey Bobkov (Minneapolis) : Strong probability distances and limit theorems
    Abstract: The lectures explore strong distances in the space of probability distributions, including total variation, relative entropy, chi squared and more general Renyi/Tsallis informational divergences, as well as relative Fisher information. Special attention is given to the distances from the normal law. The first part of the course is devoted to the general theory, and the second part to the behavior of such informational distances along convolutions and associated central limit theorem.

    Mokshay Madiman (Delaware): Entropy power and related inequalities in continuous and discrete settings
    Abstract: The lectures explore the behavior of Renyi entropies of convolutions of probability measures for a variety of ambient spaces. The first part of the course focuses on Euclidean spaces, beginning with the classical Shannon-Stam entropy power inequality and the closely related Brunn-Minkowski inequality, and developing several of the generalizations, variants, and reversals of these inequalities. The second part of the course focuses on discrete abelian groups, where one sees close connections to additive combinatorics.

    Related Materials

    (1) Survey on entropic limit theorems (M. Madiman) :

    • Lecture 1. Introduction - What is information theory? The first question that we want to address is: “What is information?” Although there are several ways in which we might think of answering this question, the main rationale behind our approach is to distinguish information from data. We think of information as something abstract that we want to convey, while we think of data as a representation of information, something that is storable/communicable. This is best understood by some examples.
    • Lecture 2. Basics / law of small numbers. Due to scheduling considerations, we postpone the proof of the entropic central limit theorem. In this lecture, we discuss basic properties of the entropy and illustrate them by proving a simple version of the law of small numbers (Poisson limit theorem). The next lecture will be devoted to Sanov’s theorem. We will return to the entropic central limit theorem in Lecture 4.
    • Lecture 3. Sanov’s theorem. The goal of this lecture is to prove one of the most basic results in large deviations theory. Our motivations are threefold: 1. It is an example of a probabilistic question where entropy naturally appears. 2.The proof we give uses ideas typical in information theory. 3. We will need it later to discuss the transportation-information inequalities (if we get there).
    • Lecture 4. Entropic CLT (1). The subject of the next lectures will be the entropic central limit theorem (entropic CLT) and its proof.
    • Lecture 5. Entropic CLT (2). The goal of this lecture is to prove monotonicity of Fisher information in the central limit theorem. Next lecture we will connect Fisher information to entropy, completing the proof of the entropic CLT.
    • Lecture 6. Entropic CLT (3). In this lecture, we complete the proof of monotonicity of the Fisher information in the CLT, and begin developing the connection with entropy. The entropic CLT will be completed in the next lecture.
    • Lecture 7. Entropic CLT (4). This lecture completes the proof of the entropic central limit theorem.
    • Lecture 8. Entropic cone and matroids. This lecture introduces the notion of the entropic cone and its connection with entropy inequalities.
    • Lecture 9. Concentration, information, transportation (1). The goal of the next two lectures is to explore the connections between concentration of measure, entropy inequalities, and optimal transportation.
    • Lecture 10. Concentration, information, transportation (2) Recall the main proposition proved in the previous lecture, which is due to Bobkov and Götze (1999).

    (2) A survey on forward and reverse entropy power inequalities, 2017.

    Organizers

    Sponsors

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    Applications are invited for a 4-year PhD studentship in Neuroscience and Machine Learning at the University of Oxford. The successful candidate will join the Oxford Interdisciplinary Bioscience DTP and will work with Andrew Saxe, Tim Behrens and Christopher Summerfield on understanding the computational mechanisms of learning in biological systems and artificial agents, and will be primarily based in the Department of Experimental Psychology. The successful candidate will have the opportunity to collaborate with an industry partner.

    Applicants should have strong quantiative skills and a background in computer science, computational neuroscience, statistics, or a related discipline. Applicants with a joint interest in both machine learning/AI research and computational neurobiology are particularly encouraged to apply.

    In the first instance please contact Dr Andrew Saxe (asaxe [at] fas.harvard.edu) to discuss your suitability for the project. The application deadline is Friday 13th July 2018 (see http://www.biodtp.ox.ac.uk/how-apply/bbsrc-artificial-intelligence-npif-studentships-2018.html for details). This project is funded for four years by the Biotechnology and Biological Sciences Research Council BBSRC and as such is only available to UK and EU candidates. Successful students will receive a stipend of no less than the standard RCUK stipend rate, currently set at £14,777 per year.

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    pyMEF: a Python library for mixtures of exponential families

    OFFICIAL WEBSITE - TUTAORIAL - DOWNLOAD

    Description

    pyMEF is a Python framework allowing to manipulate, learn, simplify and compare mixtures of exponential families. It is designed to ease the use of various exponential families in mixture models.
    See also jMEF for a Java implementation of the same kind of library and libmef for a faster C implementation.

    What are exponential families?

    An exponential family is a generic set of probability distributions that admit the following canonical distribution:

    Capture du 2018-06-08 11-19-07.png

    Exponential families are characterized by the log normalizer function F, and include the following well-known distributions: Gaussian (generic, isotropic Gaussian, diagonal Gaussian, rectified Gaussian or Wald distributions, lognormal), Poisson, Bernoulli, binomial, multinomial, Laplacian, Gamma (incl. chi-squared), Beta, exponential, Wishart, Dirichlet, Rayleigh, probability simplex, negative binomial distribution, Weibull, von Mises, Pareto distributions, skew logistic, etc.

    Mixtures of exponential families provide a generic framework for handling Gaussian mixture models (GMMs also called MoGs for mixture of Gaussians), mixture of Poisson distributions, and Laplacian mixture models as well.

    Tutorials

    A generic tutorial on the exponential families and the simplification of mixture models have been made during the workshop Matrix Information Geometries.

    More pyMEF specific tutorials are available here:
    Basic manipulation of mixture models

    Bibliography

    • Olivier Schwander, Frank Nielsen, Simplification de modèles de mélange issus d’estimateur par noyau, GRETSI 2011
    • Olivier Schwander and Frank Nielsen, pyMEF - A framework for Exponential Families in Python, in Proceedings of the 2011 IEEE Workshop on Statistical Signal Processing
    • Vincent Garcia, Frank Nielsen, and Richard Nock, Levels of details for Gaussian mixture models, in Proceedings of the Asian Conference on Computer Vision, Xi’an, China, September 2009
    • Frank Nielsen and Vincent Garcia, Statistical exponential families: A digest with flash cards, arXiV, http://arxiv.org/abs/0911.4863, November 2009
    • Frank Nielsen and Richard Nock, Sided and symmetrized Bregman centroids, in IEEE Transactions on Information Theory, 2009, 55, 2048-2059
    • Frank Nielsen, Jean-Daniel Boissonnat and Richard Nock, On Bregman Voronoi diagrams, in ACM-SIAM Symposium on Data Mining, 2007, 746-755
    • A. Banerjee, S. Merugu, I. Dhillon, and J. Ghosh, Clustering with Bregman divergences, in Journal of Machine Learning Research, 2005, 6, 234-245

    See also jMEF for a Java implementation of the same kind of library and libmef for a faster C implementation.

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    “Intelligence is the faculty of manufacturing artificial objects, especially tools to make tools, and of indefinitely varying the manufacture.” Henri Bergson

    GSI forge presents and lists packages and softwares usually opensource (Python and associated Github depositories, R and associated CRAN-R depositories) that can be useful in the statistical and informational analysis of data with a geometrical or topological approach.

    image009.png

    Venus at the Forge of Vulcan, Le Nain Brothers, Musée Saint-Denis, Reims (Vulcan is the god of fire and god of metalworking and the forge, often depicted with a blacksmith’s hammer)

    Cartan's father Joseph (1837-1917) was born in the village of Saint Victor de Morestel, which is 13 kilometers from Dolomieu. After he married Anne Cottaz (1841-1927) the family settled in Dolomieu, where Anne had lived. Joseph Cartan was the village blacksmith. Elie Cartan recalled that his childhood had passed under "blows of the anvil, which started every morning from dawn", and that "his mother, during those rare minutes when she was free from taking care of the children and the house, was working with a spinning-wheel".

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    Diffeomorphic Demons
    Diffeomorphic Demons is an efficient algorithm for the diffeomorphic registration of N dimensional images. It is based on Thirion’s demons algorithm but works on a Lie group structure on diffeomorphic transformations. Typical 3D medical images can be registered in less than three minutes on a 2 x 2.8 GHz quad-core Intel Xeon Apple Mac pro computer. Diffeomorphic demons is now included in MedINRIA‘s image fusion module. The source code has been integrated into ITK since version 3.8. A command-line software can be found on the Insight Journal. Additionally, some standalone binaries and tutorials may be found on the Stark Lab website.

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