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Call for contributions : French Regional Conference on Complex Systems

CCS – France 2021

French Regional Conference on Complex Systems

May 26 – 28, 2021

Dijon, France

You are cordially invited to submit your contribution until April 06, 2021 (Firm deadline).

 CCS – France 2021 (Conference on Complex Systems – France 2021) is the first edition of the French regional conference on complex systems organized by the French chapter of CSS (Complex Systems Society – France). It aims to promote interdisciplinary exchanges between researchers from various scientific disciplines and backgrounds (sociology, economics, history, management, archaeology, geography, linguistics, statistics, mathematics, and computer science). CCS – France 2020 is an opportunity to exchange and promote the cross-fertilization of ideas by presenting recent research work, industrial developments, and original applications. Special attention is given to research topics with a high societal impact from the perspective of complexity science.

Submission Guidelines

Finalized work (published or unpublished) and work in progress are welcome. Two types of contributions are accepted:

  • Full paper about original research
  • Extended Abstract about published or unpublished research. It is recommended to be between 2-3 pages. They should not exceed four pages.

Submissions must follow the Springer publication format available on the journal Applied Network Science in the Instructions for Authors’ instructions entry.

All contributions should be submitted in pdf format via EasyChair.

Publication

All Accepted submissions of unpublished work will be invited for publication in a special issue (fast track procedure) in one of the journals:

o   Applied Network Science edited by Springer

o   Complexity edited by Hindawi

List of Topics

Topics include, but are not limited to:

  • Foundations of complex systems
    • Self-organization, non-linear dynamics, statistical physics, mathematical modeling and simulation, conceptual frameworks, ways of thinking, methodologies and methods, philosophy of complexity, knowledge systems, Complexity and information, Dynamics and self-organization, structure and dynamics at several scales, self-similarity, fractals
  • Complex Networks
    • Structure & Dynamics, Multilayer and Multiplex Networks, Adaptive Networks, Temporal Networks, Centrality, Patterns, Cliques, Communities, Epidemics, Rumors, Control, Synchronization, Reputation, Influence, Viral Marketing, Link Prediction, Network Visualization, Network Digging, Network Embedding & Learning.
  • Neuroscience, Linguistics
    • Evolution of language, social consensus, artificial intelligence, cognitive processes & education, Narrative complexity
  • Economics & Finance
    • Game Theory, Stock Markets and Crises, Financial Systems, Risk Management, Globalization, Economics and Markets, Blockchain, Bitcoins, Markets and Employment
  • Infrastructure, planning, and environment
    • critical infrastructure, urban planning, mobility, transport and energy, smart cities, urban development, urban sciences
  • Biological and (bio)medical complexity
    • biological networks, systems biology, evolution, natural sciences, medicine and physiology, dynamics of biological coordination, aging
  • Social complexity

o   social networks, computational social sciences, socio-ecological systems, social groups, processes of change, social evolution, self-organization and democracy, socio-technical systems, collective intelligence, corporate and social structures and dynamics, organizational behavior and management, military and defense systems, social unrest, political networks, interactions between human and natural systems, diffusion/circulation of knowledge, diffusion of innovation

  • Socio-Ecological Systems
    • Global environmental change, green growth, sustainability & resilience and culture
  • Organisms and populations
    • Population biology, collective behavior of animals, ecosystems, ecology, ecological networks, microbiome, speciation, evolution
  • Engineering systems and systems of systems
    • bioengineering, modified and hybrid biological organisms, multi-agent systems, artificial life, artificial intelligence, robots, communication networks, Internet, traffic systems, distributed control, resilience, artificial resilient systems, complex systems engineering, biologically inspired engineering, synthetic biology
  • Complexity in physics and chemistry
    • quantum computing, quantum synchronization, quantum chaos, random matrix theory)

Committees

 GENERAL CHAIR

Hocine Cherifi                    LIB, UBFC, Dijon

 

ADVISORY BOARD

Cyrille Bertelle                  LITIS, Normastic, Le Havre

David Chavalarias              ISC PIF, Paris

Chantal Cherifi                   DISP, Lyon

Bertrand Jouve                  LISST, Toulouse

Hamamache Kheddouci       LIRIS, Lyon

Benjamin Renoust              Median Technologies, Sophia Antipolis

 

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Preprint : Spatial dynamics of complex urban systems within an evolutionary theory frame

Authors and affiiliations

Juste Raimbault 1,2,3, Denise Pumain 3

1 Centre for Advanced Spatial Analysis, UCL, London, United Kingdom
2 UPS CNRS 3611 ISC-PIF, Paris, France
3 UMR CNRS 8504 Géographie-cités, Paris, France

Abstract

This chapter is about Complexity and Spatial Dynamics in Urban Systems. Strong inequalities in the size of cities and the apparent difficulty of limiting their growth raise practical issues for spatial planning. At a time when new constraints in terms of limited energy and raw material resources or possible catastrophic events such as pandemics are challenging further urban expansion, it is important to consolidate the theories from various scientific disciplines to estimate to what extent the urban dynamics can be modified. While briefly reviewing the contributions to urban theories provided by the new developments in complexity sciences, we first advocate for the soundness of urban theories. Second, we develop our original approach considering spatial interaction and evolutionary path dependence as major features in the general behavior of urban entities. Third, we test these principles grounded in an evolutionary theory of urban systems by experimenting four dynamic models of urban growth calibrated on harmonized empirical data sets with comparisons across the whole world.

Keywords

spatial dynamics; complex systems; system of cities; evolutionary theory; urban growth; simulation

Consulter le preprint
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Conference Paper : Branched Structure Formation in a Decentralized Flock of Wheeled Robots

Authors and affiliation

  • Antoine  Gaget, Manchester Metropolitan University,  Manchester, UK/
  • Jean-Marie Montanier, Tinyclues, Paris, France.
  • René Doursat, Complex Systems Institute Paris Ile-de-France (ISC-PIF) Paris, France.

Abstract

Swarm robotics studies how a large number of relatively simple robots can accomplish various functions collectively and dynamically. Modular robotics concentrates on the design of specialized connected parts to perform precise tasks, while other swarms exhibit more fluid flocking and group adaptation. Here we focus on the process of morphogenesis per se, i.e. the programmable and reliable bottom-up emergence of shapes at a higher level of organization. We show that simple abstract rules of behavior executed by each agent (their “genotype”), involving message passing, virtual link creation, and force-based motion, are sufficient to generate various reproducible and scalable multi-agent branched structures (the “phenotypes”). On this basis, we propose a model of collective robot dynamics based on “morphogenetic engineering” principles, in particular an algorithm of programmable network growth, and how it allows a flock of self-propelled wheeled robots on the ground to coordinate and function together. The model is implemented in simulation and demonstrated in physical experiments with the PsiSwarm platform.

 

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Postdoc recruitment to study the gene networks driving ageing – CRI-Paris

Postdoc recruitment to study the gene networks driving ageing

The CRI-Paris currently offers a postdoc position in the areas of Network Science, Network Medicine, and Computational biology applied to the study of Ageing, to be filled 1st January 2021.

The applicant will join the team of Michael Rera (Utelife Lab) at the CRI in co-supervision with Marc Santolini (Interaction Data Lab, CRI) and Anastasios Giovannidis (CNRS, Sorbonne University, LIP6 lab). The project will focus on applying network approaches to understand and model the dynamics of gene networks driving ageing in Drosophila and Humans. In particular, the project will focus on describing ageing as a propagation of network failures in the multi-layer interactome. The project is supported by a French national ANR JCJC funding.

Duration: 24 months

Relevant backgrounds and experience:

• PhD in network science, data science, computer science, computational biology, engineering, physics or other related technical disciplines

• Advanced expertise in the use of Python and/or R and network libraries/packages

• Experience in advanced data wrangling and analysis

• Experience with the mining, analysis and visualisation of large network data

• Familiarity with manipulating large-scale biological data (i.e RNAseq, proteomics, metabolomics…)

Profile:

• Proven problem-solving skills (inquisitive mind and intellectual curiosity)

• Excellent communication, collaboration, and presentation skills

• Proactive and innovative

• Capacity to listen actively, obtain necessary input, share ideas, speak persuasively, and convey information in a clear, objective, and concise manner

• Ability to work in a team-oriented environment, and function productively in a dynamic work environment

• Take initiative, and be persistent in her/his drive for results

• Ability to adapt to changing circumstances

• Ability to breakdown undefined problems into specific, workable components

Income follows the standard salary grid with a gross monthly income of 2620 euros

Interested candidates should submit a formal application to these 3 addresses: Michael Rera , Marc Santolini and Anastasios Giovanidis consisting of (i) a current CV with past experience and programming skills highlighted (preferably with link to Github or equivalent), (ii) a brief statement of research experience and interests (max. 2 pages) and (iii) the contact information of up to two references (e-mail or phone number) with some context information (relationship to applicant). Do not hesitate to contact us for further questions as well!

We believe in community diversity as a driving force of excellence. Therefore, we strongly encourage members of underrepresented groups to apply.

ABOUT THE CRI

The Center for Research and Interdisciplinarity (CRI) experiments and spreads new ways of learning, teaching, conducting research and mobilizing collective intelligence in life, learning and digital sciences. The core mission of the CRI is to transform the way to research and acquire, share and co-create knowledge across the life, learning, and digital sciences. We are building a research collaboratory – up to 60 scientists, postdocs, PhD/master students working closely together on diverse but mutually complementary range of topics. We are guided by UN Sustainable Development Goals towards high-impact work on specific topic combining biomedicine, natural sciences, education, and digital transformation.

More info: https://research.cri-paris.org.

Project Web Page : https://projects.cri-paris.net/projects/tcLBvZjk/summary

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PhD Offer : Opinion Dynamics models – Cergy Paris Université/LPTM/CNRS

Call for PhD applications in the framework of the Labex MME-DII
Opinion Dynamics models: Heterogeneous agents in heterogeneous media.

Description:

The ubiquity of communication devices in our everyday activities, changes the way in which we interact with each other, for example, by allowing very different people, who would not have been able to share a discussion or support a common cause before, to converge on a particular action. These changes seem to affect the very notion of social interaction.

Furthermore, an increasing number of our common actions leave digital traces that are collected by different kinds of agents such as governments, scientific societies, commercial firms and NGOs, etc. The fact that the activities of human society can be massively monitored and stored is a new feature in history, and the impact of this fact on our behavior is far from trivial.

The present pandemic crisis provides an unexpected large scale terrain to study the modifications that arrive when the dynamics of social relations is suddenly modified. Regarding public opinion, it is therefore essential to understand the rules that govern its formation and diffusion, according to the different channels that connect the individuals in the society.

Opinion formation, a recurrent subject of study in social sciences, is often addressed through statistical analyses of data collected by the means of surveys or polls. By following the evolution of the statistical outcomes over time, it is possible to obtain some information about the dynamical processes underlying this phenomenon.

What we call the opinion of a society is a global property that characterises the society as a whole and has emerged from the repeated interactions among their agents. Defined in this way, opinion may be studied statistically. This approach is the mathematical realization of the ideas introduced more than one century ago by E. Durkheim[1] , who coined the notion of social fact. This concept refers to a property characterizing the whole society instead of the individuals, which emerges as an outcome of the dynamics governed by the interactions among them. Once the social fact is created, it is imposed on the members of the society who will find it very difficult to change it.

Early opinion dynamics studies mainly assumed a fully mixed population, which means that every agent may potentially interact with any other in the population, in other words, the interactions among agents were supposed to be long range. This approach, equivalent to a mean-field approximation, neglects the structure of the interactions. However, if social opinion emerges from the interactions between the agents, their structure may be relevant.

In fact, the key role of interactions had been recognized long ago by social scientists, who collected detailed data about social interactions in very small societies, using graph theory to represent them. J. Scott [2] gives a nice historical overview of network development in social sciences. With the development computing power and of Network Theory different models of social interactions have been proposed by physicists, applied mathematicians and computer scientists, thus setting a bridge between two different scientific communities[3] .

In spite of all these efforts studies taking into account the fact that the members of a society are intrinsically heterogeneous and so are their interactions are rare. In a very recent work [4] we have shown that when agent’s heterogeneities are taken into account, the outcomes of the very well-known Hagselmann-Krause dynamics are drastically changed.

In this project the selected candidate, will study the influence of heterogeneity in agents’ properties, in their interaction network, and the interplay between them. To do so she/he will combine both theoretical and data based models, and will apply concepts and methods issued from the study of Dynamical Systems, Statistical Mechanics, Agent Based Models and Network Theory.

References
1. Durkheim, E. Les Règles de la Méthode Sociologique (Les Presses universitaires Paris, France, 1967). URL http://classiques.uqac.ca/classiques/Durkheim_emile/regles_methode/regles_methode.html.
2. Scott, J. Social network analysis: developments, advances, and prospects. SOCNET 1, 21–26 (2011).
3. Castellano, C., Fortunato, S. & Loreto, V. Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591–646 (2009).
4. Schawe H. Hernández L, When open mindedness hinders consensus. Nature SciRep (to be published 2020), arXiv:2001.06877.

Working environment and conditions

The selected candidate will work under the supervision of Dr Laura Hernández at the Laboratoire de Physique Théorique et Modélisation (LPTM) UMR8089 CNRS-CYU, Paris-Seine University, France. She/he will also integrate an international and interdisciplinary team, composed by physicists, computer scientists, linguists, and human scientists, which are part of the OpLaDyn project, http://project.u-cergy.fr/~opladyn/on-going-projects/ awardee of the 4th round of the TransAtlantic Platform, Digging into Data Challenge. https://diggingintodata.org/awards/2016/news/winners-round-four-t-ap-digging-data-challenge , and will benefit of its activities.

The contract should start on September 2020 for a duration of 3 years. The basic gross salary (before taxes) is 1758 €/month. A teaching assistant mission may be assigned which involves 64h/year teaching assistant duties (typically one course each semester), and involves a supplement to the basic salary.

Requirements

Interested candidates should hold a Master in Physics or Mathematics. The application of those completing their master in 2019-2020, is possible provided they graduate before September 2020.

Applicants are expected to have modeling and programming skills, and a marked interest in both theoretical modeling and data analysis.

This is a competitive program, so high records are expected, in particular a good knowledge of Dynamical systems theory, Statistical Physics of phase transitions, and Network Theory would be highly appreciated.

The application should contain:

  • A curriculum vita.
  • An official certificate with the marks obtained at the Bachelor and the Master. Applicant who have not completed the master yet should provide a certified list of the marks obtained until now.
  • A statement of purpose, explaining the applicant’s interest in the project
  • Two recommendation letters of her/his professors or internship supervisors.

These documents should be sent by e-mail to Dr Laura Hernández before May 14th 2020 to: Laura.Hernandez_at_cyu.fr

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Offre de thèse MULITOUT – IFREMER Nantes

MULTITOUT : Concilier des objectifs de rendements durables des pêches avec des objectifs écosystémiques : points de référence multi-spécifiques de gestion des pêches et méthodes d’évaluation de scénarios de gestion multi-objectifs.

Résumé : 

La Politique Commune des Pêches d’un côté et la Directive Cadre Stratégie pour le Milieu Marin de l’autre, imposent d’atteindre conjointement des rendements maximaux soutenables pour l’ensemble des espèces capturées et un bon état écologique de l’écosystème marin. Prenant en considération la multiplicité des usages marins et les occupations spatiales variables dans l’espace et dans le temps des ressources et des usages, la planification spatiale marine est un outil incontournable pour l’atteinte de ces objectifs. Pourtant, le système actuel de réglementation des pêches reste principalement basé sur une gestion monospécifique par TAC qui ne garantit pas d’atteindre conjointement tous ces objectifs dans un contexte de pêcherie mixte. Pour basculer de manière opérationnelle dans une approche de gestion écosystémique, il est nécessaire de développer des points de référence et des règles de contrôle des captures spatiales, saisonnières, plurispécifiques et pluriflottilles. L’objet de cette thèse est de 1) développer un cadre théorique de développement de ces points de référence plurispécifiques et de ces nouvelles HCR spatiales et saisonnières, 2) de le valider avec un modèle de simulation de pêcherie, 3) de le tester sur la pêcherie mixte démersale du golfe de Gascogne et 4) de l’exploiter pour simuler des scénarios de gestion.

Mots Clés : indicateurs multi-dimensionnels, optimisation multi-critères, points de référence, règles de gestion, scénarios, dynamique spatiale et saisonnière, pêche, golfe de Gascogne, ISIS-Fish

Profil de candidature souhaité : Master avec des compétences en modélisation, statistiques, optimisation, halieutique, écologie numérique

Merci d’adresser votre candidature à Stéphanie Mahévas (smahevas_at_ifremer.fr) et Sigrid Lehuta (slehuta@ifremer.fr).

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PhD Offers : “Network science and the human brain” – INRIA

The Inria ARAMIS Lab at the Paris Brain Institute announces several PhD scholarships funded by the European Research Council (ERC).

We welcome applications to work on the theoretical development of network science methods (Multilayer networks, Temporal networks, Network controllability), as well as application oriented data-driven research in neuroimaging and neuroscience.

The ARAMIS Lab is highly interdisciplinary, hosting PhD students, postdocs and engineers with diverse backgrounds, including physics, mathematics, computer science, statistics, psychology and neuroscience.

Scholarships are fully funded for 3 years. Scientific education, professional formations, as well as generous travels awards, are available throughout the PhD. Successful applicants will start from October 2020.

Available subjects
Multilayer networks, Temporal networks, Network controllability

Project Summary
Our group is looking for PhD students in the area of network science. Our current work
spans the development of network approaches for understanding brain functioning,
characterizing neurological diseases, and discovering predictive biomarkers.

Basic Qualifications
We seek students motivated to explore the complexity of biological systems from a
network viewpoint. The ideal candidate has a physics, mathematics, computer science or
statistics MS. Familiarity with network science and neuroscience/imaging is expected.

Application Instructions
Prospective students should submit an application consisting of i) a current CV with
university grades list, ii) a brief statement of research experience and interests, and iii)
one letter of recommendation sent by the writer to: fabrizio.de-vico-fallani_at_inria.fr

+ Info : https://sites.google.com/site/devicofallanifabrizio/positions

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Offre de Thèse : Analyse expérimentale et modélisation des choix collectifs dans les groupes humains

Disciplines : Physique de la société et Ethologie quantitative

Laboratoires: Centre de Recherches sur la Cognition Animale – Equipe «Collective Animal Behavior», CNRS UMR 5169, Université Paul Sabatier, Toulouse, France (http://cbi- toulouse.fr/eng/equipe-fourcassie) & du Laboratoire de Physique Théorique — Equipe « Physique statistique des systèmes complexes », CNRS UMR 5152, Université Paul Sabatier, Toulouse, France (http://www.lpt.ups-tlse.fr/spip.php?rubrique32)

Description

Il existe chez l’Homme de très nombreuses situations dans lesquelles les décisions d’un individu sont influencées par les choix ou les décisions réalisées par d’autres individus. Par ailleurs, les processus d’influence sociale sont très largement présents dans nos sociétés numériques et souvent exploités dans les réseaux sociaux, ainsi que dans le commerce électronique sur Internet. Cependant, malgré le développement de systèmes de notation et de recommandation, l’obtention d’évaluations fiables de services ou de produits demeure problématique. Comprendre l’influence des diverses formes de traces digitales dans les processus décisionnels à l’échelle individuelle et leurs conséquences dans les dynamiques de choix collectif constitue donc un enjeu majeur pour le développement de systèmes d’information destinés à accroître les capacités de collaboration et de coordination au sein de groupes humains.

L’objectif de cette thèse sera d’étudier sous quelles conditions des interactions contrôlées entre les individus d’un groupe peuvent conduire celui-ci à trouver ou à se rapprocher de la bonne solution à un problème. Ce projet implique une collaboration entre l’équipe « Physique statistique des systèmes complexes » du Laboratoire de Physique Théorique et l’équipe « Collective Animal Behavior » du Centre de Recherches sur la Cognition Animale. Il s’agira d’étudier plus spécifiquement d’une part des processus de recherche collective d’information et d’autre part, sous quelles conditions un groupe peut optimiser collectivement ses choix dans un jeu de minorité.

Le caractère innovant de ce projet de thèse réside dans le développement d’interfaces numériques permettant le contrôle en temps réel de l’information échangée entre les sujets au sein d’un groupe et la quantification très précise de l’influence de cette information digitale sur le comportement des sujets. Le projet utilisera en particulier un système de réalité virtuelle permettant l’analyse des décisions d’un sujet en présence d’agents virtuels dont le comportement et les interactions avec le sujet réel seront contrôlés par l’expérimentateur.

La méthodologie employée consistera à caractériser et quantifier à la fois et séparément les comportements et interactions à l’échelle des individus et les comportements collectifs à l’échelle d’un groupe ; les deux échelles de phénomènes seront ensuite reliées au moyen de modèles mathématiques construits à partir des données à l’échelle individuelle. L’obtention et la validation expérimentale de modèles reproduisant les caractéristiques dynamiques des phénomènes étudiés permettra de déterminer le rôle joué par les différentes formes de traces digitales dans les propriétés apparaissant à l’échelle d’un groupe d’individus et de développer des applications de gestion intelligente d’aide à la prise de décision qui intègrent les spécificités des comportements individuels en réponse à ces traces digitales.

Compétences requises

La/le doctorant(e) participera à la réalisation des expériences, à l’analyse et à l’interprétation des données. Il/elle jouera également un rôle actif dans la construction et la simulation numérique des modèles en lien étroit avec les responsables du projet. Le profil souhaité du/de la candidat(e) est un(e) physicien ou informaticien et/ou titulaire d’un master dans une discipline pertinente à la réalisation du projet, ayant des compétences en analyse de données (type R) et en modélisation, et maîtrisant au moins un langage de programmation (Python, C++, Fortran…).

Bibliographie

Jayles B et al. (2017). How can social information improve estimation accuracy in human groups. Proc. Natl. Acad. Sci. USA, 114: 12620-12625. http://goo.gl/KCSTn1

Weitz S et al. (2012) Modeling Collective Animal Behavior with a Cognitive Perspective: A Methodological Framework. PLoS ONE, 7: e38588. https://goo.gl/9yGrhT9.

Garnier S, Gautrais J, Theraulaz G (2007) The biological principles of swarm intelligence. Swarm Intelligence, 1: 3-31.

Calovi DS et al. (2018) Disentangling and modeling interactions in fish with burst and coast swimming reveal distinct alignment and attraction behaviors. PLoS Comp. Biol. 14: e1005933. https://goo.gl/wWGXja

Killijian M-O, Pasqua R, Roy M, Tredan G, Zanon C (2016) Souk: Spatial Observation of hUman Kinetics, Computer Networks, Elsevier. http://dx.doi.org/10.1016/j.comnet.2016.08.008.

Mots clés

Comportements collectifs, choix collectifs, traces digitales, réalité virtuelle, systèmes complexes, modélisation mathématique, simulations numériques.

Type de financement

Contrat Doctoral financé par le CNRS à partir du 1er septembre 2020 et pour 36 mois

Renseignements

Les demandes de renseignements concernant cette thèse peuvent être adressées à Guy Theraulaz (guy.theraulaz_at_univ-tlse3.fr) ou Clément Sire (clement.sire_at_irsamc.ups-tlse.fr).

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Appel à candidatures pour l’école thématique CNRS ” Sciences de l’Information Géographique Reproductible

21 au 26 juin 2020 une école thématique CNRS intitulée ” Sciences de l’Information Géographique Reproductibles ” (https://sigr2020.sciencesconf.org/).

L’école thématique vise à répondre aux problématiques de production, publication, diffusion ou valorisation de traitements de données géographiques dans une démarche de recherche reproductible.

En identifiant les principales avancées conceptuelles, méthodologiques et techniques du domaine via un focus sur les méthodes de traitement de l’information géographique, cette école permettra aux participants de s’initier à la mise en œuvre de protocoles de recherche ouverts et transparents avec le logiciel libre R.

La formation s’articulera autour de cours magistraux, de séances de travail et de sessions de présentations (chaque participant.e fera un court exposé des enjeux de reproductibilité de l’analyse de l’information géographique auxquels il/elle fait face dans le cadre de ses recherches).

Grands axes de l’école thématique

  • Les méthodes d’analyse reproductibles et les concepts utiles à leur mise en place;
  • Les géotraitements et la cartographie statistique;
  • L’analyse spatiale;
  • L’analyse d’image raster et la télédétection.

Informations pratiques

  • À l’exception d’un cours et d’un atelier en anglais, l’ensemble de l’école thématique sera en français.
  • L’école thématique se tiendra du dimanche 21 au vendredi 26 juin 2020 à Saint-Pierre d’Oléron dans le centre de la Vielle Perrotine du CNRS.
  • La formation (ateliers + conférences + hébergement + repas) est gratuite pour les participants salariés du CNRS (ingénieurs ou chercheurs). Son coût est de 450€ (250€ pour les doctorants) pour les participants hors CNRS (universités et autres établissements). Les frais de transport ne sont pas compris.

Candidature

Cette école est ouverte sur candidature aux chercheurs, enseignants chercheurs, ingénieurs d’étude et de recherche, doctorants, post-doctorants, français ou étrangers.

Les prérequis sont une connaissance des méthodes et concepts du traitement de l’information géographique ou une aisance préalable dans la manipulation et le traitement de données numériques.

L’école n’étant pas conçue comme une introduction à la programmation avec R, les candidat.e.s n’ayant aucune connaissance de R ou d’un autre langage de programmation ne seront pas acceptés. En revanche le niveau d’aisance en R n’est pas un critère de sélection.

Pour participer à cette école thématique, merci de bien vouloir transmettre à l’adresse sigr2020@sciencesconf.org avant le 29 février :

  • un court CV (utiliser le modèle suivant https://sigr2020.sciencesconf.org/data/CV_SIGR.odt)
  • un exposé des enjeux de reproductibilité de l’analyse de l’information géographique auxquels il/elle fait face dans le cadre de ses recherches (une page maximum). Cette présentation permettra d’organiser les sessions d’exposés et devra être illustrée par une image (indiquez les sources de l’image).

Plus d’informations sur le site de l’école thématique : https://sigr2020.sciencesconf.org/

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Master thesis Offer in Complex systems – LPTM, Université de Cery Pontoise

Title: Investigate the role of noise and of synaptic plasticity for encoding information in spiking neuronal networks

Short Description: The project investigates the complex behaviour of a network of spiking neurons connected together. We plan to study the cooperation between the irregular activity of single cells (balanced networks) and spike time dependent plasticity (STDP), a biologically inspired learning rule for neural systems.

Duration & Funding: The expected duration is up to six months, the stage is paid around 560 euros per month.

Requirements for the students: Good knowledge of programmation (C or C++ (preferred) or Fortran or Python) with application to Statistical mechanics and Dynamical systems – Master M2 in Mathematics or Physics.

Location: Laboratoire Physique Théorique et Modélisation (LPTM) CY Cergy Paris University, Cergy-Pontoise (FR)

Contact :

– Matteo di Volo matteo.di-volo@u-cergy.fr
– Alessandro Torcini alessandro.torcini@u-cergy.f

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