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  • Geo-Sci-Info

    The poster sessions will be held every afternoon after the sessions and after the dinner in the Lounge room, such that discussions are encouraged:

    • Antonia M Frassino (Frankfurt Institute for Advanced Studies, Germany) Deep learning and the universal quantum simulator, as meant by Feynmann, are machines able to build representations and can be used as efficient generative models. The representational power of the deep neural networks has been experimentally verified while its theoretical reasons behind their power are still unclear. The generative power of the universal quantum simulators has been proved rigorously, while their actual construction is a topic of active research in Physics.
      Quantum states and the relative entropy (KL-divergence) allow unveiling the geometrical structure of the two computational models and how information shapes their representation space.
      Here we draw a parallel between the two systems, addressing the problems and the open questions.
      In particular, we focus on the landscape of the empirical risk in the training of deep neural networks and the role of entanglement entropy in quantum computation.
      The purpose of the analysis is to study how information geometry models their links with Nature.

    • Anton Mallasto (University of Copenhagen, Danemark) : We introduce a novel framework for statistical analysis of populations of non-degenerate Gaussian processes (GPs), which are natural representations of uncertain curves. This allows inherent variation or uncertainty in function-valued data to be properly incorporated in the population analysis. Using the 2-Wasserstein metric we geometrize the space of GPs with L^2 mean and covariance functions over compact index spaces. We present results on existence and uniqueness of the barycenter of a population of GPs, as well as convergence of the metric and the barycenter of their finite-dimensional counterparts. This justifies practical computations. Finally, we demonstrate our framework through experimental validation on GP datasets representing brain connectivity and climate development.

    • Hiroshi Matsuzoe (Nagoya Institute of Technology, Japan) : A survey on infinite dimensional affine differential geometry and information geometry. The main object of affine differential geometry is to study hypersurfaces or immersions that are affinely congruent in an affine space. It is known that dual affine connections and statistical manifold structures naturally arise in this framework. For example, a statistical manifold structure of an exponential family is realized by an affine hypersurface immersion, and the Kullback-Leibler divergence coincides with the affine support function. The Legendre transformation can be discussed by the codimension two affine immersion of a special kind. Therefore, the geometry of dually flat spaces can be generalized by affine differential geometry. In this presentation, we would like to give a short survey about the relations between the infinite dimensional framework of affine differential geometry and information geometry. In particular, we would like to discuss the infinite dimensional affine differential geometry of the maximal exponential families and the alpha-families.

    • Eduardo Serrano-Ensástiga (Institute for Nuclear Sciences, UNAM, Mexico) Study of the FS metric with the Majorana's stellar representation. The action of the group SO(3) in the Hilbert space of spin j H allows a principal fiber bundle structure, and split the tangent space in each point in two vectorial subspaces, the vectical and horizontal space, where the horizontal vectors are defined as the ortogonal vectors of the vertical ones with the FS metric. By the other hand, the Majorana’s stellar representation gives us a geometric view of H and its tangent bundle TH in terms of moving points on the sphere. In this representation, the vertical vectors have a clear interpretation, but the horizontal vectors don't. We expose some properties of the FS metric, the characteristics of the vertical vectors and a study of the horizontality condition in terms of the stellar representation.

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  • Geo-Sci-Info

    The Ninth International Conference on Guided Self-Organisation (GSO-2018) : Information Geometry and Statistical Physics

    March 26 - 28, 2018
    Max Planck Institute for Mathematics in the Sciences

    The goal of Guided Self-Organization (GSO) is to leverage the strengths of self-organization (i.e., its simplicity, parallelization, adaptability, robustness, scalability) while still being able to direct the outcome of the self-organizing process. GSO typically has the following features:

    (i) An increase in organization (i.e., structure and/or functionality) over time;

    (ii) Local interactions that are not explicitly guided by any external agent;

    (iii) Task-independent objectives that are combined with task-dependent constraints.

    GSO-2018 is the 9th conference in a bi-annual series on GSO. Recent research is starting to indicate that information geometry, nonequilibrium statistical physics in general, and the thermodynamics of computation in particular, all play a key role in GSO. Accordingly, a particular focus of this conference will be the interplay of those three topics as revealed by their relationship with GSO.

    The following specific topics are of special interest:

    • information-driven self-organisation
    • complex systems and networks
    • non-equilibrium statistical physics
    • non-extensive statistical mechanics
    • physics of information and computation
    • information dynamics
    • generalised entropies
    • generalised relative entropies
    • alpha geometry and alpha statistics
    • constraints and maximum entropy principle
    • information-geometric aspects of Fokker-Planck and Kolmogorov equations

    More Information

    Date and Location

    March 26 - 28, 2018
    Max Planck Institute for Mathematics in the Sciences
    Inselstraße 22
    04103 Leipzig
    see travel instructions

    Organizing Committee

    • Nihat Ay MPI for Mathematics in the Sciences Leipzig (Germany)
    • Mikhail Prokopenko University of Sydney (Australia)

    Program Committee

    • Nihat Ay, MPI for Mathematics in the Sciences, Leipzig (Germany)
    • Domenico Felice, Università degli Studi di Camerino, Camerino (Italy)
    • Carlos Gershenson, Universidad Nacional Autónoma de México, Computer Sciences Department, Mexico City (Mexico)
    • Paolo Gibilisco, Università degli Studi di Roma "Tor Vergata", Facoltà di Economia, Roma (Italy)
    • Daniel Polani, University of Hertfordshire, Department of Computer Science, Hatfield (United Kingdom)
    • Mikhail Prokopenko, University of Sydney, Sydney (Australia)
    • Richard Spinney, University of Sydney, Sydney (Australia)
    • Justin Werfel, Harvard University, Cambridge (USA)
    • Larry Yaeger, Google Inc., San Francisco (USA)
    • G. Çiğdem Yalçın, İstanbul Üniversitesi, İstanbul (Turkey)

    Administrative Contact
    Antje Vandenberg
    MPI for Mathematics in the Sciences

    posted in The Ninth International Conference on Guided Self-Organisation (GSO-2018) : Information Geometry and Statistical Physics read more
  • Geo-Sci-Info


    The Basque Centre for Applied Mathematics (BCAM),together with Ikerbasque, the Basque Foundation for Science, invites applications for one Research Professor position in Computational Fluid Dynamics and one Research Professor position in Data Science. This Research Professor call offers permanent contract positions for experienced researchers.

    ONE Research Professor position in Computational Fluid Dynamics.

    Basis and Rules:
    Application form:

    ONE Research Professor position in Data Science.

    Basis and Rules:
    Application form:

    We encourage immediate applications as the selection process will be ongoing.
    Desired skills and experience

    Candidates should provide:

    Letter of interest, including your research interest.
    2 recommendation letters (additional references may be requested during the evaluation)
    Statement of past experience (2-3 pages). Please, highlight your main results.
    Only researchers with a strong record of research will be considered. Women candidates are especially welcome.

    To submit your application please click on the "Go to application page" button.
    About the employer

    BCAM is a world-class research center on Applied Mathematics with a focus on interdisciplinary research in the frontiers of mathematics, attraction and training of talented scientists, development of new numerical and simulation methods, interaction with industry, and promotion of scientific and technological advances worldwide.

    Located in the Basque Country, it benefits from a long industrial tradition, and it is linked with the French Atlantic corridor, a region of excellence in Applied Mathematics. This context contributes to the task of building an excellence research center. BCAM counts with around 60 researchers from over 20 different countries with experience in some of the most prestigious research centers on their area, organized in 5 research areas and an administrative support team composed by 7 people.

    BCAM is a young research center that is facing its consolidation phase. In this sense, the scientific strategy of the center is based on three Scientific Platforms that have been set up in order to establish an interdisciplinary system capable of facing the challenges of Mathematical Science in a broad manner by bringing together Mathematics, Engineering and Sciences:

    • Core in Applied Mathematics: PDE, Numerical Analysis, Fourier Analysis, Algebraic Geometry, Probability and Statistics.

    • Computational Mathematics: Modeling andcomputer simulations using numerical, stochastic and Monte Carlo methods.

    • Applications of Mathematics to Industry, Social Sciences and Health Sciences.

    Regarding human resources management, BCAM core values rely on people as its main asset, so, the continuous evolution of the HR strategy is key for the success of BCAM in order to adapt to the needs of the people, so BCAM decided to launch the HR Excellence in Research process (as the implementation of the European Charter for Researchers and Code of Conduct for the Recruitment) to enhance the efficiency, effectiveness and impact of the actions that BCAM should undertake to provide an attractive and supportive environment to researchers. BCAM was awarded with the HR Excellence in Research in May 2016.

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  • pparrend

    Author: Pierre Parrend, Timothé Mazzucotelli, Florent Colin
    RESEARCH REPORT n°1, ARK:69427/03
    Saturday, 20th May, 2017
    4P-Factory E-Laboratory: the factory of the future

    Design Structure Matrices; Security Information Systems Architecture; Software Security; Secure Software Development Methodologies; Security and privacy in Complex Systems; Security Metrics and Measurement

    Building secure software is often seen as a task mainly focused on development and penetration testing. However, it requires ensuring that the system embeds robust architecture principles, on which security features and proper code can rely. Few solutions for creating and monitoring such architectures exist, and those existing are dedicated to mission- and life-critical systems. Mainstream technologies, such as web platforms, host ever increasing critical application and data, and need suitable tools for enforcing relevant architecture-level monitoring. We propose to apply the Design Structure Matrix (DSM) model to represent and analyze the structure of complex applications. DSM is both an efficient analysis tool and a convenient tool for visualising complex systems. It supports the translation of software architectures into graphs, which prove to be efficient tools for structural analysis. Guidelines for secure architectures are expressed, first qualitatively, then in a quantitative manner, as constraints on these graphs. The Archan tool for supporting DSM-based architecture monitoring is presented. Our approach is illustrated for pedagogical stakes using two toy examples, and validated on a middle-scale project for managing sensitive medical data. Archan thus enforces sound architectural principles, which are a pre-requisite for building secure systems.

    posted in CS-DC Research Reports read more
  • Geo-Sci-Info

    Ingénieur de recherche ou post doc data scientist

    spécialisé en traitement automatique du langage

    sur données massives en santé

    L’équipe projet Données Massives en Santé (15 personnes) du Laboratoire
    Traitement du Signal et de l’Image (LTSI – UMR Inserm 1099) est composée
    d’ingénieurs de recherche et développement logiciel, de data scientists
    et de professionnels de santé spécialisés en informatique médicale qui
    conçoivent et développent des méthodes et outils d’intégration et de
    fouille de données massives en santé (Big Data).

    Elle intervient sur divers projets de recherche et développement, autour
    de ces thématiques, aussi bien dans un cadre interrégional (BIGCLIN,
    Réseau des Centres de Données Cliniques), national (projets INSHARE,
    PEPS) et international (IT-Foc, FIGTEM) en synergie avec le centre
    hospitalier universitaire de Rennes.

    L’équipe a une politique de transfert technologique. Elle développe un
    partenariat industriel pour le déploiement de son système eHop (entrepôt
    et outil de fouille de données biomédicales) dans les CHU. (voir : )

    Poste et mission

    • Développer au sein de l’équipe un axe traitement des données
      textuelles médicales (recherche d’information et plus généralement
      text mining) qui puisse répondre aux cas d’usages définis avec
      l’équipe médicale.
    • Développer, implémenter et évaluer des méthodes de traitement
      automatique du langage innovantes sur très larges volumes de données
    • Participer aux cycles R D du logiciel en lien avec l’équipe
      d’ingénieur : prospective/prototypage, évaluation, conception et
      développements, transfert industriel
    • Valoriser les travaux au travers de publications scientifiques dans
      des journaux et des conférences de références.
    • Participer aux réponses d’appel d’offre recherche.

    Profil :

    • Diplômé(e) de l’enseignement supérieur (bac +5 et/ou doctorat)
    • Background en mathématique, statistique et machine Learning,
      expérience d’utilisation de larges volumes de données, expérience en
      traitement automatique du langage, capacité à conduire des études
      expérimentales, compétence en analyse de données, écriture
      scientifique, ainsi qu’en présentation et communication ;
    • Forte expérience en programmation et développement des systèmes.
    • Expériences en modélisation, en terminologies et ontologies médicales,
      python, R, Apache SPARK sont un gros plus.
    • Capacité à travailler en équipe pluridisciplinaire.
    • Environnement scientifique et technique

    Lieu : Centre des Données Clinique du CHU de Rennes / Faculté de
    médecine – Université Rennes1

    Applications : recherche clinique, épidémiologie, pharmacovigilance,
    études médico- économiques, médecine 4P

    Entrepôts de données biomédicales du biomédical du grand ouest : exemple
    à Rennes : 1,6 millions de patients, 33 millions de documents cliniques,
    330 millions d’éléments de données

    Thèmes de recherche : big data en santé, intégration et fouille de
    données, interopérabilité́ et sé- mantique, intégration des méthodes TAL,
    machine learning, analyse de cluster

    Technologies : calcul distribué, php, java, R, oracle, BD nosql,
    Framework Big data (spark), responsive framework

    Contrat de 12 mois (renouvelable) à Rennes - Poste immédiatement
    disponible (Mai 2017) Envoyer CV et lettre de motivation à
    marc.cuggia [ chez ]

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  • pparrend

    Author: Fabio Guigou, Pierre Collet, Pierre Parrend
    TECHNICAL REPORT n°69427/02
    Thursday 13th April, 2017
    4P-Factory E-Laboratory: the factory of the future

    Time series, Symbolic representation, Anomaly detection, Pattern mining

    The advent of the Big Data hype and the consistent recollection of event logs and real-time data from sensors, monitoring software and machine configuration has generated a huge amount of time-varying data in about every sector of the industry. Rule-based processing of such data has ceased to be relevant in many scenarios where anomaly detection and pattern mining have to be entirely accomplished by the machine. Since the early 2000s, the de-facto standard for representing time series has been the Symbolic Aggregate approXimation (SAX).

    In this document, we present a few algorithms using this representation for anomaly detection and motif discovery, also known as pattern mining, in such data. We propose a benchmark of anomaly detection algorithms using data from Cloud monitoring software.

    posted in CS-DC Technical Reports read more
  • Geo-Sci-Info

    • 3 Junior postdoc

    The candidates should have defended their Ph.D. thesis in the period 01/04/2015-01/07/2017.

    • 3 senior postdoc

    The candidates should have defended their Ph.D. thesis in the period 01/04/2011- 31/03/2015.

    Deadline for applications May 3.

    Please circulate this announcement among potential candidates.

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

    Le Domaine d'Intérêt Majeur Math Innov, programme labellisé par la Région Île-de-France en décembre 2016, finance 12 allocations doctorales de 3 ans en mathématiques et en informatique fondamentale dans les laboratoires de son réseau (FSMP, FMJH, IHP, Fédération Bézout et Paris-Seine) pour la rentrée 2017.
    L'appel d'offre est ouvert jusqu'au 28 mai 2017, 23h59 heure de Paris.

    Les candidatures se font exclusivement en ligne via le formulaire (cf. lien ci-dessous)

    Retrouvez ici le détail de l'offre :
    Et le formulaire de candidature :

    Pour plus de questions n'hésitez pas à contacter l'équipe gestionnaire : contact [at]
    Bien cordialement,

    L'équipe de la Fondation Sciences Mathématiques de Paris
    Gestionnaire du DIM Math Innov
    IHP - 11 rue Pierre et Marie Curie
    75231 Paris Cedex 05
    contact [at]
    Tél : 33 (0)1 44 27 66 48

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

    Postdoctoral position in Pattern Recognition, Machine Learning and Computer Vision for the analysis of neuroimaging, biomedical and biological data - [ Postdoc ]
    Workplace: IIT - Genova - Italy

    The Pattern Analysis and Computer Vision department (PAVIS) at IIT ( is looking for a highly qualified candidate in the field of Computer Vision, Pattern Recognition, Machine Learning, and Image Analysis interested and possibly with some experience on Biomedical Data Analysis.

    PAVIS focuses on the analysis and understanding of multimodal data, like signals, images, videos and patterns in general, having a wide expertise on image and signal processing, computer vision, pattern recognition and machine learning. The attention is on the design of intelligent systems for real applications, especially related, but not limited, to surveillance security and biomedical imaging.

    The main mission of PAVIS is to design and develop innovative computational frameworks for advanced image-based and video-based data analysis, characterized by the use of smart sensors and advanced imaging devices. PAVIS plays an active role in supporting other research units at IIT providing scientists in Neuroscience, Nanophysics and other IIT departments/centers with ad hoc solutions.

    For this reason, previous multidisciplinary experience is an added value which will be properly considered.

    The biomedical imaging team is involved in activities concerning:

    • Biomedical image analysis;

    • Analysis of multimodal neuroimaging data (functional and structural connectomics);

    • Biological signal/image/video processing;

    • Analysis of microscope optical imaging;

    • Animal behavior analysis (classification and abstraction).

    The ideal candidate must be knowledgeable within one or more of the following subjects: probabilistic graphical models, topic models and Bayesian non-parametric, relational learning, deep learning, RBMs, neural networks and auto-encoders, sparse and dictionary learning, kernel methods and manifold learning, graph-based learning and spectral analysis.

    Candidates to this position have, therefore, a Ph.D. in computer vision, machine learning, pattern recognition or related areas, and research experience and qualification should follow the same lines.

    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. A keen interest in biomedical applications is of course necessary. Strong programming skill is required.
    Experience in the preparation and management of research proposals (EU, US, national) and a few years of postdoc experience, either in academia or industrial lab, will also be duly considered. The scientist is expected to publish his/her research results in leading international journals and conferences. She/he is also expected to contribute to the set-up of new project proposals, participate in funding activities, supervising PhD candidates and collaborate with scientists from different disciplines.

    The position is offered for a period of 2 years. Salary will be commensurate to qualification and experience and in line with international standard.

    Further details and informal enquires can be made by email to pavis [ chez ] quoting PAVIS-PD 73154 as reference number in the subject.

    Please send you application both to pavis [ chez ] and to applications [ chez ], quoting PAVIS-PD 73154 as reference number, along with a curriculum listing all publications (possibly including pdf of your most representative publications), a research statement describing your previous research experience and outlining its relevance to the above topics and names of 2 referees.

    This call will remain open and applications will be reviewed until the position is filled, but for full consideration please apply by April 15, 2017.

    In order to comply with the Italian law (art. 23 of Privacy Law of the Italian Legislative Decree n. 196/03), we have to kindly ask the candidate to give his/her consent to allow IIT to process his/her personal data. We inform you that the information you provide will be used solely for the purpose of assessing your professional profile to meet the requirements of Istituto Italiano di Tecnologia.

    Your data will be processed by Istituto Italiano di Tecnologia, with headquarters in Genoa, Via Morego 30, acting as the Data Holder, using computer and paper based means, observing the rules on protection of personal data, including those relating to the security of data. Please also note that, pursuant to art.7 of Legislative Decree 196/2003, you may exercise your rights at any time as a party concerned by contacting the Data Manager.

    Istituto Italiano di Tecnologia is an Equal Opportunity Employer that actively seeks diversity in the workforce.

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  • Geo-Sci-Info

    Job offer
    Machine Learning Research Engineer

    Do you want to contribute to a fast-growing company at the cutting edge of innovation between optics and artificial intelligence? LightOn is looking for a Research Engineer specialized in Machine Learning / Data Science for the development of new optical co-processors for Artificial Intelligence.
    Within the R D team and reporting to the CTO, your main duties will include :
    ● the design of statistical learning algorithms that take advantage of LightOn processors,
    ● algorithm testing on LightOn’s processors,
    ● managing and interacting with industrial partners,
    ● interacting with developers of the software layer for network access (API),
    ● interfacing with the hardware developing team,
    ● carrying out rapid prototyping activities in synchronization with the rest of the team.

    REQUIRED PROFILE Engineering Degree (MSc or PhD) in Machine Learning / Data Science. An industry experience would be a plus.

    Technical skills (required) : You should
    ● Have some theoretical knowledge and hand-on experience in unsupervised or supervised machine learning (eg Deep Neural Networks),
    ● Have some experience on how to process and make sense of very large amounts of data,
    ● Be proficient in scientific programming (Python, C ++, Matlab, ...).
    ● Be a user of one or more Machine Learning/Deep Learning framework(s) (Scikit-learn, TensorFlow, Keras, Theano, Torch, etc)

    A significant interest in one or more of the following topics would be a plus:
    ● automated search for hyper-parameters,
    ● digital electronics or FPGA programming.

    In order to work in a small startup such as LightOn, you will also need to creative and pragmatic, have some team spirit and some good communication skills.

    CONDITIONS This position is for a full-time employment, that can start as soon as a possible. Salary will based on technical skills and experience. The candidate must have the right to work in the EU. We cannot pay for relocation costs.

    To respond to this offer, please send an e-mail to jobs [ chez ] with [ML Engineer] in the subject line. Please attach a resume and cover letter both in PDF.

    THE COMPANY Founded in 2016, LightOn ( is a technology start-up that develops a new generation of optical co-processors designed to accelerate the low power Artificial Intelligence algorithms for massive amounts of data. The technology developed by LightOn originates from the ESPCI and Ecole Normale Supérieure laboratories. LightOn won in 2016 the best Digital Tech startup from the City of Paris. We are located in the center of Paris within the Agoranov incubator.

    Offre d'emploi CDI
    Ingénieur de recherche en Machine Learning (H/F)

    Vous souhaitez contribuer au développement d'une entreprise en pleine croissance en vous impliquant dans des projets à la pointe de l'innovation à l’interface entre l’optique et l’intelligence artificielle ? LightOn recherche un(e) ingénieur de recherche / docteur spécialisé(e) en Apprentissage Statistique (Machine Learning / Data Science) pour développer nos nouveaux co-processeurs optiques pour l’intelligence artificielle. Au sein d’une équipe de R D, sous l’autorité du directeur technique, vos principales missions sont de :
    • Définir et concevoir des algorithmes d’apprentissage statistique tirant avantage des processeurs LightOn,
    • Définir et réaliser les moyens de test des algorithmes sur les prototypes, de façon opérationnelle,
    • Gérer les partenaires industriels (rédaction des spécifications, suivi technique, planning et qualité),
    • Définir, maîtriser et fiabiliser les interfaces utilisateurs, en fonction d’usages spécifiques,
    • Interagir avec les développeurs de la couche logicielle pour un accès réseau (API),
    • Interfacer avec l’équipe développant les prototypes expérimentaux,
    • Réaliser des activités de prototypage, de test et d'optimisation en lien avec le reste de l’équipe.

    ENTREPRISE LightOn (, créée en 2016, est une start-up technologique qui développe une nouvelle technologie de co-processeurs optiques, conçus pour accélérer les algorithmes d'Intelligence Artificielle sur de très grandes masses de données, à moindre consommation énergétique. La technologie développée par LightOn est issue des laboratoires de l'ESPCI et de l'Ecole Normale Supérieure. LightOn a remporté en 2016 le Grand Prix de l’Innovation de la ville de Paris, dans la catégorie Technologies Numériques. Nos locaux sont situés au sein de l’incubateur Agoranov, 96bis Bd Raspail 75006 Paris.

    CONDITIONS Emploi en CDI. Début dès que possible. Rémunération selon profil et expérience.

    Ingénieur ou Master en Machine Learning / Data Science. Un doctorat ou une expérience industrielle serait un plus.

    Compétences techniques nécessaires :
    • Connaissance théorique et pratique de l’Apprentissage supervisé (par exemple Réseaux de Neurones Profonds) ou non supervisé.
    • Gestion de très grands volumes de données, techniques de réduction dimensionnelle,
    • Programmation scientifique (Python, C++, Matlab,...).
    • Utilisation d’un ou plusieurs framework(s) de Machine Learning (Scikit-learn, TensorFlow, Keras, etc).

    Les plus
    Un intérêt marqué pour un ou plusieurs des thèmes suivants : recherche automatisée d’hyper-paramètres, projections aléatoires, traitement du signal, électronique numérique, programmation sur FPGA.

    Etat d’esprit
    Esprit d’initiative, créativité, pragmatisme, rigueur, dynamisme et autonomie. Esprit d’équipe et bonne communication (anglais courant), attrait pour les startups. Le candidat doit être autorisé à travailler dans un pays de l’UE.

    Pour répondre à cette offre, merci d’envoyer un e-mail à jobs [ chez ] avec [ingénieur ML] dans le titre, en joignant un CV et en présentant vos motivations.

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