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    Capture du 2020-03-09 02-09-50.png
    Appel à Candidatures
    Prix de Thèse Systèmes Complexes 2020

    Depuis 2017, l'Institut des Systèmes Complexes de Paris IdF (ISC-PIF) et ses partenaires décernent annuellement un prix de thèse dont l'objectif est de mettre à l'honneur la recherche dans le domaine des systèmes complexes et de distinguer les travaux de jeunes chercheur·euse.s particulièrement prometteur·euse.s.

    Ce prix est organisé en collaboration avec l'Académie d'Excellence "Systèmes Complexes" de l'Université Côte d'Azur et l’Institut Rhônalpin des Systèmes Complexes (IXXI), avec le soutien du CNRS et de la région Île-de-France.

    Nous vous invitons cordialement à diffuser largement cet appel ou à nominer un.e ou plusieurs candidat.e.s ayant soutenu leurs thèses entre janvier et décembre 2019, dans une école doctorale française.

    Veuillez noter que les jeunes docteurs de nationalité étrangère peuvent participer à cette sélection.

    Pour nominer un.e ou plusieurs candidat.e.s, merci de nous transmettre leurs coordonnées en réponse de ce mail, en les mettant éventuellement en copie.

    Ci dessous, les détails de l'appel à candidature que nous vous invitons cordialement à partager.
    Télécharger le pdf de l'appel

    ÉLÉGIBILITÉ

    Les thèses éligibles auront été soutenues entre janvier et décembre 2019, dans une école doctorale française. Les jeunes docteurs de nationalité étrangère peuvent participer à cette sélection. Les thèses rédigées en anglais sont acceptées par le jury.

    Pour pouvoir concourir à ce prix, les candidat.e.s doivent faire la preuve de la pertinence de leurs travaux par rapport à l'approche systèmes complexes et doivent aborder un ou plusieurs objets et questions propres à la recherche Systèmes Complexes : voir tous les détails du Prix de Thèse 2020 sur notre site.

    CANDIDATURES

    Les candidats et candidates devront remplir leur candidature en ligne avant le 22 mars 2020 à minuit.

    Documents demandés : CV et lettre de motivation, rapport du jury de thèse et pré-rapports des rapporteurs, résumé de la thèse.

    JURY

    Les candidat.e.s sélectionnés seront invités à présenter leur travaux devant un jury interdisciplinaire composé de personnalités reconnues dans le domaine. Les auditions auront lieu à l'ISC-PIF (Paris 13e), le 22 mars 2020.

    Les critères pris en compte par le jury pour sélectionner les lauréat.e.s sont l’importance et l’originalité des contributions dans le domaine des systèmes complexes, la qualité du manuscrit et, éventuellement, l’interdisciplinarité du travail.

    CALENDRIER

    • 22 mars 2020 : Date limite des dépôts de candidatures
    • 15 mai 2020 : Annonce des finalistes
    • 17 juin 2020 : Audition des finalistes et remise des prix
      + d'infos sur le site

    COORDINATEUR.TRICE.S

    • Annick Vignes, CAMS (CNRS EHESS), LISIS.
    • Julien Randon-Furling, SAMM (Univ. Paris 1 Panthéon Sorbonne), Fédération Parisienne de Modélisation Mathématique (CNRS).

    NOUS ÉCRIRE

    contactcom[at]iscpif.fr
    Capture du 2020-03-09 02-07-36.png

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    OFFICIAL WEBSITE - REGISTRATION -

    Welcome from the Chair
    The concept of entropy emerges initially from the scope of physics, but it is now clear that entropy is deeply related to information theory and the process of inference. Today, entropic techniques have found a broad spectrum of applications in all branches of science.

    The conference will be organized into six sessions, which reflect the inter-disciplinary nature of entropy and its applications:

    • Statistical Physics
    • Information Theory, Probability and Statistics
    • Thermodynamics
    • Quantum Information and Foundations
    • Complex Systems
    • Entropy in Multidisciplinary Applications

    The inter-disciplinary and multi-disciplinary nature of contributions from both theoretical and applied perspectives are welcome, including papers addressing conceptual and methodological developments, as well as new applications of entropy and information theory.

    All accepted abstracts will be published in the proceedings of the conference. Moreover, participants are cordially invited to contribute with a full manuscript to Entropy Special Issue "Entropy: The Scientific Tool of the 21st Century".

    Special Issue Submission deadline: 30 October 2020
    Papers presented at the conference will be granted a 30% discount!

    The conference is sponsored by MDPI, the publisher of the open-access journal Entropy and follows the very successful meeting Entropy 2018: From Physics to Information Sciences and Geometry held in May 2018 in Barcelona, Spain.

    We very much look forward to seeing you at this exciting meeting in Porto.

    Please feel free to download our Conference Poster.

    Conference Secretariat

    • Ms. Yuejiao Hu
    • Ms. Connie Xiong
    • Ms. Stefanie Li
      Email: entropy2020 [at] mdpi.com

    Conference Chairs

    • Professor J. A. Tenreiro Machado, Institute of Engineering (ISEP), Department of Electrical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal, Website

    Conference Committee

    • Professor Walter Lacarbonara, Department of Structural and Geotechnical Engineering , Sapienza University of Rome, Rome, Italy
    • Professor Jose C. Principe, Computational NeuroEngineering Lab, University of Florida, Gainesville, FL, USA
    • Prof. Dr. Gian Paolo Beretta, Department of Mechanical and Industrial Engineering, University of Brescia, Brescia, Italy
    • Professor Geert Verdoolaege, Research Unit Nuclear Fusion, Department of Applied Physics, Ghent University, Ghent, Belgium
    • Prof. Dr. William B. Sherwin, Evolution & Ecology Research Centre, School of Biological Earth and Environmental Science, UNSW Sydney, Sydney, Australia
    • Prof. Dr. Ali Mohammad-Djafari, Former Research Director, CNRS, Paris, France
    • Dr. Ginestra Bianconi, Queen Mary University of London, London, UK

    Invited Speakers

    • Prof. Dr. Kevin H. Knuth, University at Albany (SUNY), Albany, NY, USA
    • Prof. Dr. Miguel Rubi, University of Barcelona, Barcelona, Spain
    • Prof. Dr. Philip Broadbridge, La Trobe University, Melbourne, Australia
    • Prof. Dr. Marcel Ausloos, University of Leicester, Leicester, UK
    • Dr. Remo Garattini, University of Bergamo, Bergamo, Italy
    • Prof. Dr. Nihat Ay, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
    • Prof. Dr. Luca Gammaitoni, Università degli Studi di Perugia, Perugia, Italy
    • Prof. Dr. Abraham Marmur, Technion-Israel Institute of Technology, Haifa, Israel
    • Dr. Rosario Lo Franco, Università di Palermo, Italy
    • Prof. Dr. Miguel A. F. Sanjuán, Universidad Rey Juan Carlos, Madrid, Spain

    Conference Organizers

    • Nídia Caetano, Institute of Engineering (ISEP), Department of Chemical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal
    • Carlos Felgueiras, Institute of Engineering (ISEP), Department of Electrical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal
    • Ana Maria Madureira, Instituto Superior de Engenharia do Porto (ISEP), Porto, Portugal
    • Luís M. Afonso, ISRC (Interdisciplinary Studies Research Center) , Instituto Superior de Engenharia do Porto, Porto, Portugal
    • Alexandra M.F. Galhano, Institute of Engineering (ISEP), Department of Electrical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal

    Sessions

    • SESSION 1. Statistical Physics
      classical, quantum, and relativistic statistical mechanics; kinetics theory; dynamical processes and relaxation phenomena; geometric applications to statistical physics; classical and quantum stochastic processes; combinatorial aspects of statistical physics and quantum field theory; quantum information and entangled states. Statistical physics of complex and disordered systems: biophysics, genomics, ecological and evolutionary systems, climate and earth models (including seismology); traffic flow; nonlinear time series analysis; big data analysis and algorithm problems; networks and graphs; random and fractal systems; pattern formation; collective phenomena in economic and social systems. Show all accepted abstracts (12)
    • SESSION 2. Information Theory, Probability and Statistics
      Shannon entropy; Kullback-Leibler divergence; channel capacity; source coding; channel coding; algorithmic complexity theory; algorithmic information theory; information–theoretic security; information geometry; Bayesian inference; information theoretic learning; deep learning. Show all accepted abstracts (18)
    • SESSION 3. Thermodynamics
      heat transfer; Carnot cycle; heat engine; thermodynamic temperature; entropy generation; Clausius equality/inequality; equilibrium/non-equilibrium thermodynamics; work availability; exergy; laws of thermodynamics. Show all accepted abstracts (26)
    • SESSION 4. Quantum Information and Foundations
      quantum foundations; quantum probability and non-Kolmogorov models; entanglement entropy; Eigenstate thermalization; quantum phase transitions; topological order; black hole information paradox; the holographic principle; quantum coherent transport; quantum coherence in biological phenomena. Show all accepted abstracts (9)
    • SESSION 5. Complex Systems
      complexity; nonlinearity; fractionality; nonlinear dynamics; fractional calculus; chaos; big data; networks; cybernetics; biology. Show all accepted abstracts (18)
    • SESSION 6. Entropy in Multidisciplinary Applications
      signal processing and data analysis; entropy and complexity in biology; geosciences; environment; social network; economy and finance. Show all accepted abstracts (40)

    Sponsor
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    posted in Entropy 2020: The Scientific Tool of the 21st Century read more
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    The division of science at New York University in Abu Dhabi is currently advertising two-years postdoctoral positions to work with me on subjects in high dimensional probability and related fields.
    More information can be found at the following link: https://apply.interfolio.com/74217 where the applications should be submitted. Please do not hesitate to share the message with potential candidates and feel free to contact me directly for further information.

    The anticipated start date is September 1, 2020. Applications should be received before March 15 to be guaranteed full consideration. (Candidates should please ask their referees to send their letters of reference by that date.)

    posted in Jobs offers - Call for projects read more
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    Open GPU Data Science | RAPIDS

    OFFICIAL WEBSITE - DOWLOADS - MANUALS

    ACCELERATED DATA SCIENCE
    The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs.
    Learn more

    SCALE OUT ON GPUS
    Seamlessly scale from GPU workstations to multi-GPU servers and multi-node clusters with Dask.
    Learn more about Dask

    PYTHON INTEGRATION
    Accelerate your Python data science toolchain with minimal code changes and no new tools to learn.

    TOP MODEL ACCURACY
    Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.

    REDUCED TRAINING TIME
    Drastically improve your productivity with more interactive data science.
    Learn more about XGBoost

    OPEN SOURCE
    RAPIDS is an open source project. Supported by NVIDIA, it also relies on numba, apache arrow, and many more open source projects.
    Learn more

    posted in GSI FORGE read more
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    Capture du 2020-02-16 13-55-41.png
    OFFICIAL WEBSITE - MANUAL - GIT and DOWNLOADS
    This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.

    It provides the following solvers:

    • OT Network Flow solver for the linear program/ Earth Movers Distance [1].
    • Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional * GPU implementation (requires cupy).
    • Sinkhorn divergence [23] and entropic regularization OT from empirical data.
    • Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17].
    • Non regularized Wasserstein barycenters [16] with LP solver (only small scale).
    • Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4].
    • Optimal transport for domain adaptation with group lasso regularization [5]
    • Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
    • Linear OT [14] and Joint OT matrix and mapping estimation [8].
    • Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt).
    • Gromov-Wasserstein distances and barycenters ([13] and regularized [12])
    • Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
    • Non regularized free support Wasserstein barycenters [20].
    • Unbalanced OT with KL relaxation distance and barycenter [10, 25].

    Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.

    Using and citing the toolbox
    If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference:

    @misc{flamary2017pot,
    title={POT Python Optimal Transport library},
    author={Flamary, R{'e}mi and Courty, Nicolas},
    url={https://github.com/rflamary/POT},
    year={2017}
    }

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    lease find below the announcement of three positions (two M2
    internships and one post-doc position) opened in Nancy :

    https://drive.google.com/file/d/1nzkCIoWcotQhtFBPY6CmVMnDoUfH3c1k/view

    https://drive.google.com/file/d/18s60b0FDxp8r3-UpuBWsZczpsdKA6lL9/view

    https://drive.google.com/file/d/1qNK1yUv_Ws4qoFOITFJ9ug7YNkwk7uLo/view

    %%%%%%%%%%%%%%%%%%%%%%%%%%%
    %%%%%%%%%%%%%%%%%%%%%%%%%%%

    =====================================
    PhD opening at the University of Groningen
    =====================================

    The group of Probability and Statistics at the University of Groningen invites applications for a

    4-year PhD position

    During your PhD you will work at the forefront of research in topological data analysis and spatial random networks. While the previous years have brought forth outstanding progress in the description of large combinatorial random graphs, the understanding of random structures with spatial dependencies is still in its infancy. A key component of your research will be to identify regimes that are amenable to a rigorous mathematical description in terms of central limit theorems and large deviation principles.

    Modern materials science is a major driving force behind the interest in topological data analysis and spatial random networks, since the insights gained from appropriate mathematical models make it possible to reduce the number of time-consuming experiments. With the newly established CogniGron, the University of Groningen has created an interdisciplinary research center with the goal of making progress towards cognitive computing. Hence, you will have the unique opportunity to interact broadly with colleagues outside mathematics. You will also be expected to contribute to the teaching at the faculty.

    Hence, if you have graduated with an outstanding Master's degree (or are about to do so soon) and are committed to working on fundamental research questions, we are very much looking forward to your application. Any background knowledge in large deviation theory, random networks or central limit theorems will help you to get a smoother start.

    We offer a full-time PhD position for 4 years. The salary and working hours are determined according to the Collective Labour Agreement for Dutch Universities. In 2019, PhD candidates started with € 2,325 gross per month; for a candidate in the fourth year, this figure rises to € 2,972. In addition to that, the salary scheme comprised 8% of the yearly salary as holiday bonus and 8.3% as end of your bonus.

    We are an equal opportunity employer that values diversity. We have adopted an active policy to increase the number of female scientists across all disciplines of the university.

    We envision that you join us on April 1, 2020 or later. Please send us until January 27, 2020 your cover letter, CV, Master's thesis (or a preliminary version) and transcript of records. All documents should be sent to dr. Christian Hirsch: c.p.hirsch@rug.nl, who will also be happy to answer your questions on the position.

    %%%%%%%%%%%%%%%%%%%%%%%%%%%
    %%%%%%%%%%%%%%%%%%%%%%%%%%%

    STATLAB–CANSSI–CRM POSTDOCTORAL FELLOWSHIP 2020-2021

    Applications are invited for a StatLab–CANSSI–CRM Postdoctoral Fellowship in Statistics. To be eligible, candidates should have received their PhD after June 30, 2016, and must have fulfilled all PhD requirements by the time of taking up the award. The successful candidate must be supervised by at least one Regular Member of StatLab and be based in a member’s institution for the tenure of the award. A list of StatLab members can be found here: http://www.crm.umontreal.ca/labo/stat/membres/

    The fellowship is worth $45 000. A successful candidate will be designated a StatLab–CANSSI–CRM Postdoctoral Fellow for a period of one year, starting any time between June 1 and September 30, 2020.

    An application includes: (i) a presentation letter including in particular a description of research interests and research program; (ii) the candidate’s CV; (iii) electronic copies of articles, theses, research reports, etc. Candidates must prepare the application using the guidelines presented here: http://www.crm.umontreal.ca/labo/stat/en/postdoctoral-fellows/

    and send it to coeurjolly.jean-francois@uqam.ca by January 31, 2020. Candidates should arrange as well to have at least two letters of recommendation sent independently.

    PDF announcement is enclosed to this email.

    BOURSE POSTDOCTORALE STATLAB–INCASS–CRM 2020-2021

    Un appel à candidatures est lancé pour une bourse postdoctorale StatLab–INCASS–CRM en statistique. Pour être éligible, un candidat doit avoir obtenu son doctorat après le 30 juin 2016 et avoir complété toutes les exigences du programme de doctorat au moment de débuter le stage. Il ou elle doit être supervisé par au moins un membre régulier de StatLab et être inscrit dans l’établissement d’un tel membre pendant la période de validité de la bourse. Pour une liste complète des membres de StatLab, voir http://www.crm.umontreal.ca/labo/stat/membres/

    La bourse est d’une valeur de 45 000$. Le boursier portera le titre de stagiaire postdoctoral StatLab–INCASS–CRM pendant une période d’un an pouvant commencer à tout moment entre le 1er juin et le 30 septembre 2020.

    Un dossier de candidature comprend: (i) une lettre de présentation incluant notamment une description des intérêts de recherche, un programme de recherche; (ii) le CV du candidat; (iii) copies électroniques d'articles, thèse, rapport de recherche, etc. Les candidats doivent préparer leur dossier en suivant les indications disponibles ici: http://www.crm.umontreal.ca/labo/stat/stagiaires-postdoctoraux/

    et envoyer leur dossier à coeurjolly.jean-francois@uqam.ca avant le 31 janvier 2020. Les candidats doivent également s'arranger pour transmettre au moins deux lettres de recommandation, envoyées indépendamment.

    L'annonce officielle au format pdf est jointe à ce courriel.

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    Lors de la prochaine campagne synchronisée, un poste MCF 26/27
    "Statistique pour la science des données" sera publié à l'IUT
    d'Avignon, département STID. La personne recrutée intègrera le
    département STID et l'équipe de Statistique du Laboratoire de
    Mathématiques d'Avignon (LMA). Le profil détaillé est disponible via le
    lien :

    https://math.univ-avignon.fr/wp-content/uploads/sites/23/2020/01/IUT_MCF26-27_FOP_0840685N_41573-1.pdf

    Je vous remercie par avance de faire suivre cette annonce autour de
    vous. Nous invitons toutes les personnes intéressées à contacter
    Delphine Blanke (responsable STID & équipe de Statistique) et Céline
    Lacaux (directrice du LMA).

    Amitiés, Céline.

    PS : Je vous prie de nous excuser pour les réceptions multiples de cette
    annonce.

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  • Geo-Sci-Info

    stageIECLCRAN.pdf
    postdocPositionIECLATILF.pdf

    The Faculty of Electrical Engineering, Mathematics and Computer Science
    at the University of Twente, The Netherlands, invites applications for a
    fully funded

    PhD studentship in spatial statistics

    in the project `Data driven risk management for fire services' funded
    by the Dutch Research Council.

    This project aims to develop a flexible hierarchical statistical
    framework for the analysis and prediction of various types of fires
    and will be carried out in close cooperation with the Twente Fire
    Brigade. It will deliver a model for spatio-temporal inference
    (including monitoring, filtering and prediction) that is able to
    recognize patterns in historic data and convert these to a prediction.
    The results can be used for both repression and prevention purposes,
    including improving vehicle and personnel planning in the fire stations,
    informing the right people at the right time and evaluating the
    effectivity of public awareness campaigns.

    Candidates are required to have a Masterʼs degree (or an equivalent one)
    in mathematics or a related discipline and a solid background in
    probability and statistics. They should have excellent grades, research
    talent (as proven by their thesis), an excellent command of English and
    good academic writing and presentation skills.

    The terms of employment and salary are in accordance with the Dutch
    Collective Labour Agreement for universities.

    To apply, please send a letter of motivation, a list of your BSc and
    MSc courses with grades and either a copy of your Master's thesis if
    available or a copy of your Bachelor's thesis accompanied by an outline
    of your Master's thesis to

    Dr.ir. M. de Graaf (m.degraaf [at] utwente.nl) or
    Prof.dr. M.N.M. van Lieshout (colette [at] cwi.nl).

    from whom further information can also be obtained.

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  • Geo-Sci-Info

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    W3. Complex Networks: Hidden Geometry and Dynamics
    Workshop organized by: B. Tadic and N. Gupteo

    The influence of network structure on dynamics in many complex systems has been demonstrated in numerous studies with a detailed analysis of empirical data and theoretical approaches. Recent studies of networks representing various complex systems from the brain to large-scale social dynamics have revealed their higher-order architecture, which can be described as aggregates of simplexes (triangles, tetrahedrons, and higher cliques). Beyond the standard graph theory, these hidden structures are quantified by algebraic topology methods. This Special Session will bring together presentations exploring different complex systems concerning a) the structure of simplicial complexes, including the properties of the underlying topological graph (network); b) dynamic processes in a particular or co-evolving network structure; c) information topology and graph representations of time series. We expect research results based on empirical data analysis and theoretical and numerical modeling. The aim is to cover recent developments in these areas of research and to stimulate discussion towards an in-depth understanding of the role of higher-order connectivity in the emergence of functional properties of complex systems.

    posted in Sigmaphi 2020 read more
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    S3. Information Geometry and Deformed Statistics
    Special Session organized by J. Zhang and H. Matsuzoe

    Information geometry provides a suite of differential geometric tools for studying statistical inference, information theory, and machine learning models. Key notions such as statistical manifolds (with Fisher information as to its Riemannian metric), Hessian geometry, biorthogonal coordinates have links to statistical mechanics, thermodynamics, geometric mechanics, etc. As sequel to the special session (organized by Johnston, Matsuzoe, Ruppeiner, and Wada) at SigmaPhi2017, this Session at SigmaPhi2020 will explore differential geometric characterizations of probabilistic models arising from a variety of setting including stochastic thermodynamics, condensed matter physics, cosmology and high-energy physics, etc. Of particular interest are deformed statistical models, models which deviate from exponential family through parameterization (e.g., kappa-exponential model, q-exponential model, Renyi model) and their associated deformed entropy, cross-entropy, and divergences. The Session will also welcome contributions from related disciplines of statistical machine learning, dynamics and control, optimal transport and Wasserstein geometry, etc.

    posted in Sigmaphi 2020 read more

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