• Geo-Sci-Info

    APPLICATION WEBPAGE

    Job description

    The University of Limerick (UL) with over 15,000 students and 1,400 staff is an energetic and enterprising institution with a proud record of innovation and excellence in education, research and scholarship. The dynamic, entrepreneurial and pioneering values which drive UL’s mission and strategy ensures that we capitalise on local, national and international engagement and connectivity. We are renowned for providing an outstanding student experience and conducting leading edge research. Our commitment is to make a difference by shaping the future through educating and empowering our students. UL is situated on a superb riverside campus of over 130 hectares with the River Shannon as a unifying focal point. Outstanding recreational, cultural and sporting facilities further enhance this exceptional learning and research environment.

    Applications are invited for the following position:

    Faculty of Science + Engineering

    Department of Mathematics & Statistics

    Lecturer and Lecturer below the bar in Statistics & Data Analytics

    Contract Type: Lecturer – Multiannual (Post 1)

    Contract Type: Lecturer below the bar (Post 2) - Tenure Track (five year fixed term).

    During the term of the contract the successful applicant will have the opportunity to apply for tenure in accordance with the University's Policy and Procedures for Granting Multi-annual Status to Tenure Track Academic Staff

    Salary Scale: Lecturer €52,187 - €83,038 p.a.

    Salary Scale: Lecturer below the bar €39,118 - €53,782 p.a.

    Further information for applicants and application material is available online.

    The closing date for receipt of applications is Monday, 3rd December 2018.

    Applications must be completed online before 12 noon, Irish Standard Time on the closing date.

    Please email erecruitment@ul.ie if you experience any difficulties

    Applications are welcome from suitably qualified candidates.

    The University of Limerick holds a Bronze Athena SWAN award in recognition of our commitment to advancing equality in higher education. The University is an equal opportunities employer and is committed to selection on merit welcoming applicants from all sections of the community. The University has a range of initiatives to support a family friendly working environment, including flexible working.

    “The University of Limerick has implemented a “Smoke and Vape Free Campus Policy”. Smoking and vaping in all forms is prohibited.”

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  • Geo-Sci-Info

    OFFICIAL WEBSITE APPLY
    The Siri Search team is creating groundbreaking technology for algorithmic search, machine learning, NLP, and artificial intelligence. The features we build are redefining how hundreds of millions of people use their computers and mobile devices to search and find what they are looking for.

    Siri’s universal search engine powers search features across a variety of Apple products, including Siri, Spotlight, Safari, Messages and Lookup. We work with one of the most exciting high performance computing environments, with petabytes of data, millions of queries per second, and have an opportunity to imagine and build products that delight our customers every single day.

    Now imagine what you could do at Apple?

    Key Qualifications
    Industry experience in a Data Science, Machine Learning or Natural Language Processing
    Ability to illuminate complex problems with data analysis
    Proven product success derived from research and analysis results
    Familiarity with Hadoop, Mapreduce and similar technologies
    Experience with machine learning models and systems like Tensorflow
    Programming language like Python or Go
    Experience with Search/Information Retrieval is a plus
    Good communication with internal and external teams
    Fluency in at least one of French, German, Italian, Spanish
    Fluency in English

    Description

    This role is part of a growing team so we're you will have the opportunity to work in a few areas including Data Science, ML and NLP. Collaborating with team members in Europe and around the world you can work on many areas including:

    Perform data mining to support new features - Analyze large datasets to glean actionable insights - Design classifiers and ranking algorithms - Perform language processing and query analysis - Perform ad-hoc statistical analysis - Present results of analysis to team and leadership across Apple - Craft metrics to measure the success of the service

    If this is you, we'd love to hear from you.

    Education & Experience

    MS or Ph.D. in Data Mining, Machine Learning, Statistics, Natural Language Processing (in European languages), Operations Research or related field

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  • Geo-Sci-Info

    Capture du 2018-11-26 23-17-57.png

    OFFICIAL WEBSITE
    DOWNLOAD POSTER

    Bienvenue

    Le GRETSI et le GdR ISIS organisent depuis 2006 une École d'Été annuelle en traitement du signal et des images. Ouverte à toute personne intéressée (académique ou industrielle), elle s'adresse prioritairement à des doctorants ou chercheurs en début de carrière, et a pour but de présenter une synthèse ainsi que les avancées les plus récentes dans un thème de recherche d'actualité. Cette École d'Été a lieu tous les ans et a pour cadre le magnifique village de Peyresq, perché à 1500 mètres d'altitude sur un éperon rocheux des Alpes de Haute Provence (http://www.peiresc.org).

    Thème de l'école 2019

    La session 2019 a pour thème :

    Géométrie de l'information pour le traitement du signal et des images

    La géométrie de l’information est un thème qui a généré une activité croissante dans la communauté signal et images. Parmi les thématiques incluses dans la géométrie de l’information, on compte par exemple : la définition de distances ou divergences sur des espaces courbes et les applications en classification, les statistiques sur les groupes et les variétés avec des applications en tracking, filtrage et estimation; ainsi que la caractérisation des performances des estimateurs de matrices de covariance. Le nombre des applications est croissant et les potentialités importantes, mais il faut constater que les méthodologies et concepts impliqués en géométrie de l’information ne font pas partie de beaucoup de cursus. Cette École d’Été envisage de proposer aux doctorants (en priorité) une introduction aux concepts de la théorie (géométrie différentielle) ainsi qu’un état des lieux de leurs applications en traitement des signaux et des images.
    L'École comporte à la fois des cours tutoriaux et des sessions ouvertes permettant aux participants de présenter leurs travaux et de confronter leurs idées.
    Vous pouvez télécharger l'affiche et diffuser l'information autour de vous.

    Cours
    L'École comportera à la fois des cours tutoriaux, ainsi que des sessions ouvertes permettant aux participants de présenter leurs travaux et de confronter leurs idées.
    L'emploi du temps détaillé de l'École sera prochainement disponible.
    Programme prévisionnel

    1. Introduction aux outils de géométrie différentielle et optimisation en traitement des données (5h)
    Conférencier : P.A. Absil (University of Louvain, Belgium)

    2. Géométrie de l’information et ses applications (5h)
    Conférencier : F. Nielsen (Sony Computer Science Laboratories Inc & Ecole Polytechnique)

    3. Statistiques géométriques et leurs applications aux formes anatomiques (5h)
    Conférencier : X. Pennec (INRIA Sophia Antipolis)

    4. Estimation récursive sur les variétés Riemanniennes (2h)
    Conférencier : S. Said (Université de Bordeaux)

    5. Bornes de Cramér-Rao intrinsèques et matrices de covariance (2h)
    Conférencier : A. Renaux (Université Paris Saclay)

    6. Les structures élémentaires de la géométrie de l'information et la métrique de Fisher-Koszul-Souriau : exemples d'applications pour le signal radar (2h)
    Conférencier : F. Barbaresco (Thales)

    Inscriptions
    Les demandes d'inscriptions à l'École d'Été seront ouvertes à partir de la fin Novembre 2018.
    L'École d'Été est ouverte à toute personne intéressée, académique ou industrielle. Le nombre de participants étant toutefois limité par la capacité d'accueil du lieu, une priorité sera donnée aux doctorants, aux chercheurs en début de carrière et aux industriels partenaires du GdR ISIS.
    Une participation financière couvrant les frais d'hébergement et de restauration est demandée.
    La participation aux frais est de 350€ pour les doctorants et de 600€ pour les autres (chercheurs titulaires, ingénieurs, post-doctorants, industriels).

    Dates importantes

    • novembre 2018: Ouverture du service d'enregistrement des demandes d'inscription.
    • 18 février 2019: Clôture du service d'enregistrement des demandes d'inscription.
    • 19 mars 2019: Notification des inscriptions. Ouverture du service des inscriptions définitives.
    • 3 mai 2019: Fermeture du service des inscriptions définitives.
    • 30 juin - 6 juillet 2019: École d'Eté.

    Comite d'Organisation
    Présidence

    • Patrick Flandrin Directeur de Recherche CNRS, Laboratoire de Physique, ENS de Lyon.
    • Cédric Richard Professeur des Universités, Laboratoire Lagrange, Université de Nice.

    Direction Scientifique

    Contacts
    Pour toute demande de renseignement, veuillez nous envoyer un mèl à
    peyresq19_l AT gretsi.fr

    Acces
    Quand arriver à Peyresq (et en repartir) ?
    Pour des raisons pratiques d'organisation, l'arrivée à Peyresq devra se faire impérativement dans l'après-midi ou le début de soirée du dimanche 30 juin (pas avant pour cause d'occupation du site, et pas après car les cours commenceront le lundi matin à 9h00).
    Comment accéder à Peyresq (et en repartir) ?
    1- Par bus Un bus gratuit sera mis à la disposition des participants pour effectuer directement les trajets entre Nice et Peyresq (2 heures environ). Aller : départ de la gare SNCF de Nice à 17h puis du terminal 2 de l'aéroport de Nice le dimanche 30 juin à 17h30.
    Retour : départ de Peyresq le samedi 6 juillet à 9h00, passage au terminal 2 de l'aéroport de Nice à 11h30 puis en gare SNCF à 12h.
    2- Par le train: Se rendre à la "Gare du Sud (des Chemins de Fer de Provence)", 4 rue Alfred Binet, située à 15 minutes environ de la Gare principale de Nice Prendre le train des Pignes" (qui mène à Digne) et descendre à Annot. Les horaires sont consultables sur le site des Chemins de Fer de Provence. Il est à noter que la liaison entre Annot et Peyresq (20 km) nécessite alors un taxi, qu'il est prudent de réserver (http://www.itaxis.fr/annot-04240.htm).
    3- Par la route : Coordonnées GPS : (N 44° 04' 02" - E 06° 37' 04") Attention : une fois arrivé à Peyresq, il est impératif de se garer à l'extérieur du village.

    Sponsors
    Capture du 2018-11-26 23-33-01.png

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  • Geo-Sci-Info

    Capture du 2018-11-25 13-52-07.png

    OFFICIAL WEBSITE

    The Numerical Tours of Data Sciences

    The Numerical Tours of Data Sciences, by Gabriel Peyré, gather Matlab, Python, Julia and R experiments to explore modern mathematical data sciences. They cover data sciences in a broad sense, including imaging, machine learning, computer vision and computer graphics. It showcases application of numerical and mathematical methods such as convex optimization, PDEs, optimal transport, inverse problems, sparsity, etc. The tours are complemented by slides of courses detailing the theory and the algorithms.

    Numerical Tours now in R
    Link - 35 R tours available
    Posted by Gabriel Peyré on February 26, 2018

    Numerical Tours on Machine Learning
    Link - 4 new Matlab and Python tours
    Posted by Gabriel Peyré on August 11, 2017

    Numerical Tours now in Julia
    Link - 30 Julia tours available
    Posted by Gabriel Peyré on August 5, 2017

    Numerical Tours now in Python
    Link - 30 Python tours available
    Posted by Gabriel Peyré on September 17, 2016

    New Python Tours
    Optimization by Laurent Condat
    Posted by Gabriel Peyré on June 14, 2016

    Capture du 2018-11-25 13-59-26.png
    Numerical Tours now in R
    OFFICIAL WEBPAGE
    The R tours, that can be browsed as HTML pages, but can also be downloaded as Jupyter notebooks. Please read the installation page for more information about how to run these tours.

    Basics

    Wavelets

    Approximation, Coding and Compression

    Denoising

    Inverse Problems

    Optimization

    Machine Learning

    Shapes

    Audio Processing

    Computer Graphics

    Mesh Parameterization and Deformation

    Geodesic Processing

    Optimal Transport

    Capture du 2018-11-25 14-31-08.png

    OFFICIAL WEBPAGE

    hese are the Python tours, that can be browsed as HTML pages, but can also be downloaded as Jupyter notebooks. Please read the installation page for more information about how to run these tours.

    Basics

    Wavelets

    Approximation, Coding and Compression

    Denoising

    Inverse Problems

    Optimization

    Shapes

    Audio Processing

    Computer Graphics

    Mesh Parameterization and Deformation

    Geodesic Processing

    Optimal Transport

    Machine Learning

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  • Geo-Sci-Info

    Postdoctoral position in Computer Vision & Machine Learning - [ Postdoc ] BC 75470
    Workplace: Genova, IIT, Italy
    Added on: 26/06/2018 - Expires on 31/12/2018

    The Pattern Analysis and Computer Vision Research Line (PAVIS) at IIT in Genova is looking for a highly qualified post doc with a strong background in Computer Vision, Pattern Recognition and Machine Learning, with particular emphasis on recognition, video analysis, behavior understanding, and prediction. As the activities may be carried out in collaboration with other IIT research units, the previous multidisciplinary experience is an added value which will be duly considered.
    The main mission of PAVIS is to design and develop innovative image- and video-based intelligent systems, characterized by the use of highly functional smart sensors and advanced data analytics features. PAVIS also plays an active role in supporting the other IIT research units providing scientists in Neuroscience, Nanophysics and other IIT departments/centers with ad-hoc solutions.
    To this end, the group is involved in activities concerning computer vision and pattern recognition, machine learning, multimodal\multimedia data analysis and sensor fusion, and embedded computer vision systems. The lab will pursue this goal by working collaboratively and in cooperation with external private and public partners.

    In particular, this call aims at consolidating PAVIS expertise in the video surveillance area and especially on action/activity recognition and scene understanding from video sequences and other sensory modalities.
    In particular, the following topics are of interest:
    Analysis of static and dynamic scenes.
    Recognition (objects, scenes, actions, events, etc.) and reconstruction.
    Behavior Analysis and Activity Recognition (individuals, groups, crowd).
    Prediction of intentions.
    Domain Adaptation.
    Multimodal data analysis
    Zero-shot Learning

    From the methodological standpoint, the ideal candidate should be familiar with one or more of the following subjects (it’s not an exhaustive list): Deep Learning, Graphical Models, Topic Models, Representation/Feature Learning, Sparse and Dictionary Learning, Clustering, Kernel methods, Manifold Learning and Statistical and Probabilistic Models in general.
    Candidates to this position have a Ph.D. in Computer Vision, Machine Learning, Pattern Recognition or related areas. Research experience and qualification in computer vision and pattern recognition/machine learning are clearly a must and evidence of top quality research on the above-specified areas in the form of published papers in top conferences/journals and/or patents is mandatory.
    Moreover, experience in the preparation and management of research proposals (EU, US, national) and industrial research projects, a few years of postdoc experience, either in academia or in an industrial lab, will also be duly considered. The winning candidate will also be asked to contribute to setting up new (funding) project proposals and will participate in funding activities.
    He/she is also expected to publish his/her research results in leading international journals and conferences, supervise Ph.D. candidates and collaborate with other scientists, also with different expertise.
    Salary will be commensurate to qualification and experience and in line with international standards.
    Further details and informal inquiries can be made by email to pavis@iit.it quoting PAVIS-PD 75470 as the reference number.
    Please send your completed application forms by December 31, 2018. The application must include: a curriculum listing all publications (possibly including a pdf of your most representative publications), a research statement describing your previous research experience and outlining its relevance to the above topics and the name of 2 referees by email to pavis@iit.it quoting PAVIS-PD 75470 as the reference number.

    IIT was established in 2003 and successfully created a large-scale infrastructure in Genova, a network of 10-state-of-the-art laboratories countrywide and recruited an international staff of about 1100 people from more than 50 countries. IIT's research endeavor focuses on high-tech and innovation, representing the forefront of technology with possible applications from medicine to industry, computer science, robotics, life sciences, and nanobiotechnologies.
    We inform you that the information you provide will be used solely for the purposes of evaluating and selecting professional profiles in order to meet the requirements of Istituto Italiano di Tecnologia.
    Your data will be processed by Istituto Italiano di Tecnologia, based in Genoa, Via Morego 30, acting as Data Controller, in compliance with the rules on protection of personal data, including those related to data security.
    Please also note that, pursuant to articles 15 et. seq. of European Regulation no. 679/2016 (General Data Protection Regulation), you may exercise your rights at any time by contacting the Data Protection Officer (phone +39 010 71781 - email: dpo@iit.it).
    Istituto Italiano di Tecnologia is an Equal Opportunity Employer that actively seeks diversity in the workforce.

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  • Geo-Sci-Info

    Capture du 2018-11-18 21-45-27.png
    Venue
    ENAC, Toulouse (France)
    7, avenue Edouard BelinCS 54005
    31055 Toulouse Cedex 4
    France
    GPS ccordinates GPS : 43.565156, 1.479281

    https://goo.gl/maps/D6KQBVTdVcT2

    Ecole Nationale de l'Aviation Civile
    7, avenue Edouard Belin CS 54005
    31055 Toulouse Cedex 4
    http://www.enac.fr/en https://www.youtube.com/watch?v=DwkJKuYuCXY

    Access

    Shuttle from Toulouse-Blagnac Airport

    Free "Airport" shuttle, reserved for ENAC students, teachers and speakers according to availability.
    Boarding on presentation of notification or student / trainee card.

    By public transport

    All information, maps and directions for public transportation in Toulouse are available on Tisseo website ///

    • N°68 - to La terrasse / Métro Ramonville
    • N°78 - to Université Paul Sabatier / Lycée St Orens
    • N°37 - to Jolimont / Métro Ramonville

    Subway - Line B

    • Get off at Faculté de pharmacieFaculty and take bus N°78 to Lycée St Orens
    • Get off at Ramonville-Saint-Agne and take bus N°68 to "La terrasse"

    Subway - Line A

    Get off at Jolimont and take bus N°37 to Ramonville Metro or bus N°68

    By car
    Take outer ring road (towards "Montpellier"), then follow "Toulouse center / Foix / Tarbes" (green sign). Exit N°20, follow "Complexe scientifique Rangueil".
    GPS : 43.565156, 1.479281

    planenac.png

    picenac1.jpg
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    picenac4.jpg
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    Social events
    Welcome Coktail
    will take place 27th August at "Mairie de Toulouse Palace” gsi2019-se1.jpg

    Gala Diner
    will take place 28th August at Hôtel-Dieu Saint-Jacques in Salle Des Colonnes. »
    gsi2019-se3.jpg gsi2019-se2.jpg gsi2019-se4.jpg

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  • Geo-Sci-Info

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    Conference Co-chairs:

    Frank Nielsen- Sony CSL, Japan, Ecole Polytechnique, France Frank.png
    Frédéric Barbaresco - President of SEE ISIC Club Ingénierie des Systèmes d’Information et de Communications, Thales Land & Air Systems, Limours, France Frédéric.png

    Local organizing committee:

    Scientific Committee (To be consolidated)

    • Bijan Afsari – Johns Hopkins University
    • Pierre-Antoine Absil – Université Catholique de Louvain
    • Stephanie Allasonnière – Paris-Diderot University
    • Jesus Angulo – Mines ParisTech
    • Marc Arnaudon – Bordeaux University
    • John Armstrong – King’s College London
    • Anne Auger – Ecole Polytechnique
    • Nihat Ay – Max Planck Institute
    • Roger Balian – CEA
    • Frédéric Barbaresco – Thales Land & Air Systems
    • Pierre Baudot – MEDIAN
    • Daniel Bennequin – Paris-Diderot University
    • Joel Bensoam – IRCAM
    • Yannick Berthoumieu – Bordeaux University
    • Jeremie Bigot – Bordeaux University
    • Silvere Bonnabel – Mines ParisTech
    • Michel Broniatowski – Sorbonne University
    • Michel Boyom – Montpellier University
    • Marius Buliga – Simion Stoilow Institute of Mathematics of the Romanian Academy
    • Laurent Cohen – Paris Dauphine University
    • Ana Bela Cruzeiro – Universidade de Lisboa
    • Remco Duits - Eindhoven University of Technology
    • Stanley Durrleman – INRIA
    • Alfred Galichon – New York University
    • Fabrice Gamboa – Institut Mathématique de Toulouse
    • Jean-Pierre Gazeau – Paris Diderot University
    • François Gay-Balmaz – ENS Ulm
    • Mark Girolami – Imperial College London
    • Susan Holmes – Stanford University
    • Jérémie J. Jakubowicz – Telecom SudParis
    • Jean Lerbet – Evry University
    • Nicolas Le Bihan – Grenoble University
    • Luigi Malago – Romanian Institute of Science and Technology
    • Jonathan Manton - The University of Melbourne
    • Gaetan Marceau-Caron – MILA R&D and Tech Transfer
    • Matilde Marcolli - CALTECH
    • Jean-François Marcotorchino – Sorbonne University
    • Charles-Michel Marle – Sorbonne University
    • Hiroshi Matsuzoe - Nagoya Institute of Technology
    • Jean-Marie Mirebeau – Paris Orsay University
    • Ali Mohammad-Djafari – Centrale Supelec
    • Antonio Mucherino - IRISA, University of Rennes 1
    • Florence Nicol - ENAC
    • Frank Nielsen - Ecole Polytechnique, Paris-Saclay University
    • Richard Nock - Université Antilles-Guyane
    • Yann Ollivier – FACEBOOK FAIR Paris
    • Steve Oudot – INRIA
    • Pierre Pansu – Paris-Saclay University
    • Xavier Pennec - INRIA
    • Giovanni Pistone – CarloAlberto University
    • Stephane Puechmorel - ENAC
    • Olivier Rioul – Telecom ParisTech
    • Gery de Saxcé – Lille University
    • Salem Said – Bordeaux University
    • Rodolphe Sepulchre – Liège University
    • Olivier Schwander – Sorbonne University
    • Stefan Sommer – Copenhagen University
    • Dominique Spehner – Grenoble University
    • Alain Trouvé – Ecole Normale Supérieure Paris-Saclay
    • Geert Verdoolaege – Ghent University
    • Rene Vidal – Johns Hopkins University
    • Jun Zhang – University of Michigan, Ann Arbor

    Contact
    Please use the form below to contact them electronically.

    • ALIDOR Valérie
      SEE – Congress
      Société de l'Electricité, de l'Electronique
      et des Technologies de l'Information
      et de la Communication
      17 rue de l'Amiral Hamelin
      75783 Paris Cedex16
      Phone: +33 (0)1 56 90 37 02
    • Frank NIELSEN
      Professor (PhD 1996, Habilitation 2006)
      Campus de l'École Polytechnique
      1, rue Honoré d'Estienne d'Orves
      Bâtiment Alan Turing
      91120 Palaiseau
      Phone: +33 (0)1 77 57 80 70
      (Office 2028)

    • Frédéric BARBARESCO
      THALES AIR SYSTEMS
      Technical Directorate
      Advanced Department Developments
      Voie Pierre-Gilles de Gennes, F-91470 Limours, FRANCE
      Phone: +33 (0)6 30 07 14 19

    Capture du 2018-11-18 21-57-34.png

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  • Geo-Sci-Info

    Capture du 2018-11-18 21-45-27.png
    PROBABILITY & GEOMETRY OF CHANCE (invention by Pierre de FERMAT and Blaise PASCAL in letters of their correspondence)
    Seminal Blaise Pascal paper – ALEAE GEOMETRIA, De compositione aleae in ludis ipsi subjectis, Celeberrimae matheseos Academiae Parisiensi – 1654. « ... inviter les savants géomètres à traiter nos problèmes avec le soucis de la commodité et de l’agrément : qu’ils écartent tout ce qui n’a rien à voir avec la pénétration de l’esprit, seule qualité dont nous faisons grand cas et que nous nous sommes proposé d’éprouver et de couronner » Blaise Pascal – Deuxième Lettre sur la roulette, Paris, 19 Juillet 1658

    DOWNLOAD THE CALL FOR PAPER - PDF

    Submission dates

    • Deadline for 8 pages SPRINGER LNCS format: 18th of February 2019
    • Notification of acceptance: 22nd of April 2019
    • Final paper submission: 15th of June 2019

    Submission page : https://easychair.org/conferences/?conf=gsi2019.

    GSI2019 Call for Papers
    GSI2019 Word splnproc1703
    GSI2019 Latex-llncs2e template
    GSI2019 Copyright Form Springer

    Capture du 2018-11-18 21-57-34.png

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  • Geo-Sci-Info

    Capture du 2018-11-18 21-45-27.png
    OFFICIAL WEBSITE

    DOWNLOAD POSTER

    4th conference on Geometric Science of Information - 27 August 2019 - 29 August 2019 ENAC, Toulouse (France)

    As for GSI’13, GSI’15 and GSI’17, the objective of this SEE GSI’19 conference, hosted in Toulouse at ENAC, is to bring together pure/applied mathematicians and engineers, with common interest for Geometric tools and their applications for Information analysis.
    It emphasizes an active participation of young researchers to discuss emerging areas of collaborative research on “Geometric Science of Information and their Applications”.
    Current and ongoing uses of Information Geometry Manifolds in applied mathematics are the following: Advanced Signal/Image/Video Processing, Complex Data Modeling and Analysis, Information Ranking and Retrieval, Coding, Cognitive Systems, Optimal Control, Statistics on Manifolds, Topology/Machine/Deep Learning, Artificial Intelligence, Speech/sound recognition, natural language treatment, Big Data Analytics, Learning for Robotics, etc., which are substantially relevant for industry.
    The Conference will be therefore held in areas of topics of mutual interest with the aim to:

    • Provide an overview on the most recent state-of-the-art
    • Exchange mathematical information/knowledge/expertise in the area
    • Identify research areas/applications for future collaboration

    This conference will be an interdisciplinary event and will unify skills from Geometry, Probability and Information Theory. Proceedings are published in Springer's Lecture Note in Computer Science (LNCS) series. SPRINGER will sponsor Best paper Award GSI’19.
    Gala Diner will take place at Hôtel-Dieu Saint-Jacques in Salle Des Colonnes.

    Provisional topics of interests:

    • Probability on Riemannian Manifolds
    • Optimization on Manifold
    • Shape Space
    • Statistics on non-linear data
    • Lie Group Machine Learning
    • Harmonic Analysis on Lie Groups
    • Statistical Manifold & Hessian Information Geometry
    • Monotone Embedding in Information Geometry
    • Non-parametric Information Geometry
    • Computational Information Geometry
    • Divergence Geometry
    • Optimal Transport
    • Geometric Deep Learning
    • Geometry of Hamiltonian Monte Carlo
    • Information Topology
    • Geometric & (Poly)Symplectic Integrators
    • Geometric structures in thermodynamics and statistical physics
    • Contact Geometry & Hamiltonian Control
    • Geometric and structure preserving discretizations
    • Geometry of Quantum States
    • Geodesic Methods with Constraints
    • Probability Density Estimation & Sampling in High Dimension
    • Geometry of Graphs and Networks
    • Distance Geometry
    • Geometry of Tensor-Valued Data
    • Geometric Mechanics
    • Geometric Robotics & Learning
    • Geometry in Neuroscience & Cognitive Sciences

    A special session will deal with:

    • Geometric Science of Information Libraries (geomstats, pyRiemann , …)

    Important dates

    • Deadline for 8 pages SPRINGER LNCS format: 18th of February 2019
    • Notification of acceptance: 22nd of April 2019
    • Final paper submission: 15th of June 2019

    Provisional program of Invited Speakers:
    History Session: TBC on “Fermat, Pascal & the Geometry of Chance”, Frédéric Barbaresco & Michel Boyom “Tribute to Jean-Louis Koszul (who passed away in January 2018)”
    Invited Honorary speaker: TBC, Guest Honorary speaker: TBC, and 3 keynotes Speakers: TBC
    (TBC: To be Confirmed)

    Sponsors
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  • Geo-Sci-Info

    Information.jpg

    OFFICIAL WEBSITE

    Programme
    L’information
    Samedi 17 novembre 2018
    Amphi Hermite, Institut Henri Poincaré

    • Kirone Mallick - Thermodynamique et information • 10h
    • Olivier Rioul - La théorie de l’information sans peine • 11h
    • Sergio Ciliberto - Landauer et le démon de Maxwell • 14h
    • Elham Kashefi - Quantum Verification • 15h
    • Christophe Salomon - La simulation quantique • 16h

    posted in Bourbaphy Seminar - INFORMATION read more
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    Capture du 2018-11-18 21-05-18.png

    OFFICIAL WEBSITE

    The workshop will bring together experts in geometric mechanics and optimal transport, with emphasis on stochastic aspects. The goal is to explore parallel connections between the two fields such as, for example, the Schrödinger problem and the Monge-Kantorovich theory.

    Speakers:

    • Alexis Arnaudon (Imperial College)

    • Marc Arnaudon (Univ. Bordeaux)

    • Yann Brenier (École polytechnique Paris)

    • Giovanni Conforti (École polytechnique Paris)

    • Shizan Fang (Univ. Dijon)

    • François Gay-Balmaz (ENS Paris)

    • Ivan Gentil (Univ. Lyon)

    • Rémi Lassalle (Univ. Paris Dauphine)

    • Christian Léonard (Univ. Paris Nanterre)

    • Luca Nenna (Univ. Paris Sud)

    • Gabriel Peyré (ENS Paris)

    • Nicolas Privault (NTU Singapore)

    • Tudor Ratiu (Univ. Shanghai Jiao Tong & EPFL)

    • Luigia Ripani (Univ. Lyon)

    • Sylvie Roelly (Inst. Math. Potsdam)

    • Esmeralda Sousa Dias (IST Univ. Lisboa)

    • Luca Tamanini (SISSA Trieste)

    • François-Xavier Vialard (Univ. Paris Dauphine)

    • Pierre Vuillermot (Univ. Lisboa & IECL Nancy)

      to be confirmed

    Aims and scope:

    Organizing committee: A.B. Cruzeiro, L. Monsaingeon, J.-C. Zambrini

    Contact: L. Monsaingeon

    Dowload the poster of the conference here!

    Capture du 2018-11-18 21-10-24.png

    posted in From Stochastic Geometric Mechanics to Mass Transportation Problems read more
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    Principal AI Researcher

    Job Link

    We have an opening for a principal researcher in the Autodesk AI Lab!

    The Team
    The Autodesk AI Lab is part of the AI & Robotics group at Autodesk, a rapidly growing team of over 20 researchers and engineers heading up Autodesk’s work in AI, Computational Science and Robotics. This position is based at our scenic Pier-9 AI Lab, on the water next to the Exploratorium in San Francisco.

    The Opportunity
    You will be a senior researcher focusing on problems related to geometry understanding,
    manipulation and synthesis.
    Our AI Labs focus on research in: deep learning, control systems, simulation and knowledge
    representation applied to diverse areas such as: geometry, robotics, advanced sensing, design
    exploration and sustainable engineering or construction practices. The labs also host product
    engineers resulting in early productization of our research, so you can see your work in action.
    A sampling of AI Lab projects:
    • BrickBot – https://www.fastcompany.com/90204615/autodesks-lego-model-building-
    robot-is-the-future-of-manufacturing
    • Auto Sketching and Vectorization – https://canvasdrawer.autodeskresearch.com/
    • Topology Optimization for Specific Manufacturing Processes –
    https://www.autodesk.com/customer-stories/general-motors-generative-design

    Job Requirements
    A successful candidate should have the following:
    • An MS or PhD in a field related to Machine Learning such as: Computer Science,
    Mathematics, Statistics or Physics
    • Significant doctoral or post-doctoral research experience or 5 or greater years of work
    experience
    • Solid theoretical background in geometry and geometric methods. e.g. shape analysis,
    topology, differential geometry, discrete geometry, functional mapping, etc.•

    Good background in statistical methods for Machine Learning. e.g. Bayesian methods,
    HMMs, Graphical Models, dimensional reduction, clustering, classification, regression
    techniques, etc.
    Familiarity with Deep Learning techniques. e.g. Network architectures; regularization
    techniques; learning techniques; loss-functions; optimization strategies; etc.
    Familiarity with one or more typical deep learning frameworks: TensorFlow, Caffe,
    MxNet, TORCH, Chainer, etc.
    Strong coding abilities in: Python and C/C++
    Good communication skills and an awareness of how to communicate data and results
    effectively
    Comfortable working in newly forming ambiguous areas where learning and adaptability
    are key skills
    At times, the ability to lead and rally stakeholders and team members
    Preferred Qualifications & Experience
    Experience in some of the below areas would also be beneficial:
    • Reinforcement Learning and other areas of Control Theory
    • Distributed Systems and High Performance Computing methods
    • Advanced simulation methods such as: FEA, CFD, Shape and Design Optimization,
    Photo-Realistic Rendering, etc.
    • Knowledge Representation (semantic models, graph databases, etc.)

    About the AI Lab
    Autodesk is the ideal environment for applying advanced Machine Learning techniques to: learn
    from an incredibly rich world of data; predict and synthesize solutions typically beyond human
    abilities; and create new levels of automation in how things are physically built. Given the
    broad variety of AI problems faced by Autodesk and our clients, we created a centralized facility
    to concentrate the research and engineering work behind these solutions ... The Autodesk AI
    Lab. The Lab brings together AI Researchers, Software Engineers and specialists in various
    problem areas to create novel AI solutions in all the areas mentioned above and more. They
    work closely with experts in: geometric modeling, simulation systems, robotics, knowledge
    representation, sensing and computer vision, industrial manufacturing and construction
    techniques.
    The AI Lab works with both product teams and customers to realize these AI solutions, getting
    access to massive data streams and seeing our AI models come to life in the field!Responsibilities
    As a Principal AI Researcher in the Autodesk AI Lab you will have a range of responsibilities
    including:
    • Exploring and developing new Machine Learning models and techniques
    • Constantly reviewing relevant Machine Learning literature to identify emerging methods
    or technologies and current best practices
    • Introduce creative approaches to research topics and generates new approaches,
    perspectives and solutions to research topics
    • Planning and designing research projects: specifying the problem and defining the
    project scope.
    • Connecting with academics and institutions to build relationships and collaborate
    • Realizing solutions through prototypes
    • Exploring new data sources and discovering techniques for best leveraging data
    • Collecting and performing data analysis to validate and further new theories and
    discoveries
    • Publishing and talking at conferences
    • Working closely with product engineers to design, develop and incorporate AI solutions
    into new products
    • Meeting with customers to understand how ML could be applied to their problems
    • Thinking strategically about research directions
    • Mentoring more junior researchers and engineers

    Description
    The work we do at Autodesk gets to touch nearly every person on the planet. By building tools
    for designing buildings, developing machines and even the latest movie, we get to influence
    and empower some of the most creative people in the world to solve problems that matter.
    We love that very often these are solutions to some of the most pressing issues the world faces:
    housing more people, reducing impact on our environment, and dramatically reducing illness
    and death in developing parts of the world.
    In serving these customers, we get to tap into and realize the potential of the rich streams of
    data from those worlds. Perhaps it’s real-time sensor data from cars or 3D scans of buildings
    as they are being constructed. In other cases, it’s about learning the language of 3D modeling
    from watching designers and then training the future intelligent design tools to make design
    more accessible. Or, it’s about automating and refining how things are physically made in the
    world, through advanced robotics, other times through controlling 3D printers or sophisticated
    milling machines.
    This is the ideal environment for applying advanced Machine Learning techniques to: learn from
    an incredibly rich world of data; predict and synthesize solutions typically beyond humanabilities; and to create new levels of automation in how things are physically built. Given the
    broad variety of AI problems faced by Autodesk we created a centralized facility to concentrate
    the research and engineering work behind these solutions ... The Autodesk AI Lab. The Lab
    brings together AI Researchers, Software Engineers and specialists in various problem areas to
    create novel AI solutions in all the areas mentioned above and more. They work closely with
    experts in: geometric modeling, simulation systems, robotics, knowledge representation,
    sensing and computer vision, industrial manufacturing and construction techniques.
    The AI Lab works with both product teams and customers to realize these AI solutions, getting
    access to massive data streams and seeing our AI models come to life in the field!

    posted in Jobs offers - Call for projects read more
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    pyRiemann GITHUB LINK

    pyRiemann is a Python machine learning library based on scikit-learn API. It provides a high-level interface for classification and manipulation of multivariate signal through Riemannian Geometry of covariance matrices.

    pyRiemann aim at being a generic package for multivariate signal classification but has been designed around applications of biosignal (M/EEG, EMG, etc) classification.

    For a brief introduction to the ideas behind the package, you can read the introductory notes. More practical information is on the installation page. You may also want to browse the example gallery to get a sense for what you can do with pyRiemann and then check out the tutorial and API reference to find out how.

    To see the code or report a bug, please visit the github repository.

    Documentation

    posted in GSI FORGE read more
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    Capture du 2018-10-14 10-06-30.png

    OFFICIAL WEBPAGE

    JEAN-MARIE SOURIAU

    JMS30avril1991-768x641.jpg

    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

    Capture du 2018-10-14 10-21-27.png

    posted in SOURIAU’19 read more
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    Capture du 2018-10-06 09-13-06.png

    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
    Capture du 2018-10-06 09-18-09.png

    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

    Capture du 2018-10-06 09-24-34.png

    posted in Foundations of Geometric Structures of Information read more
  • Geo-Sci-Info

    Capture du 2018-10-05 14-54-30.png

    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

    posted in Course - Entropy Methods - Christian Schmeiser read more
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    Capture du 2018-09-20 16-53-38.png
    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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  • Geo-Sci-Info

    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!

    posted in Jobs offers - Call for projects read more
  • Geo-Sci-Info

    Capture du 2018-09-09 13-43-53.png

    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

    posted in Random graphs and its applications for networks read more
  • Geo-Sci-Info

    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.

    posted in Jobs offers - Call for projects read more
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