Offres d’emploi

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Intitulé: Chargé.e de Recherche à Mines ParisTech
Type d’offre d’emploi: Offre de poste dans l’académique
Détails de l’offre: Un poste de CR tenure-track (3 ans avant CDI) en machine learning / statistique pour la biologie / santé au CBIO de Mines ParisTech et de l’Institut Curie. Possibilité de CDI immédiat pour un profil CR1.
Date limite de candidature: 2020-08-31
Mail de contact:
Intitulé: Physics Informed Deep Learning Learning : Destabilizing Recurrent Processes
Type d’offre d’emploi: Offre de thèse
Détails de l’offre: Numerical simulation of flow in porous media is an important tool in recent applications relevant to sustainable energy transition such as carbon capture and storage (CCS), geothermal energy or subsurface hydrogen extraction .These simulations of complex physics deal with large geological domains and time scales. Their use is therefore often limited by the computational time required. These limits are essentially due to the injection and production of fluids through the various wells in the domain which undermines the stability of the system and requires very small integration time steps to preserve numerical stability and accuracy. These events however are quite similar from one simulation to another. The objective of this research is then to use deep learning models incorporating the relevant physical equations (« physics informed deep learning » [4]) in order to predict the solution of the partial differential equations characterizing fluid flow in porous media due to well injection and production.
Date limite de candidature: 2020-12-31
Mail de contact:
Intitulé: Machine learning enhanced resolution of Navier Stokes equations on general unstructured grids
Type d’offre d’emploi: Offre de thèse
Détails de l’offre: Computational fluid dynamics relies on the numerical resolution of the Partial Differential Equations (PDE) of the Navier-Stokes problem. To that end, we use spatial and temporal integration schemes to predict the evolution of physical fields representing liquid or gas phases inside a computing domain discretized by a mesh. To handle complex physical phenomena at small scales, either in time or in space, it may be necessary to manage several millions of grid cells and time steps of the order of microseconds that make simulation times very long even with parallel execution. For many domains (combustion simulation, multiphase flow in chemical reactors, cooling system design), the CFD code simulation time is a major issue that makes any improvement on the performance of simulation tools significant.This PhD position aims at exploring and adapting the scientific machine learning emerging field of research that combines traditional scientific computing with machine learning to enhance the performance of CFD simulator. The idea is to leverage the latest developments in deep learning applied to CFD simulations and extend these to deal with unstructured meshes.
Date limite de candidature: 2020-12-31
Mail de contact:
Intitulé: Post-doctoral position – Machine Learning for identifying biomarkers of response to RT +/- immunothe
Type d’offre d’emploi: Offre de post-doc
Détails de l’offre: Context: A postdoctoral position is available to work on a PRT-K (Programme de Recherche Translationnel) project entitled « Prediction of response to combined chemoradiation +/- immunotherapy treatments in locally advanced cervical cancers: development of a radiomic signature of lymphocyte infiltrate » in the Molecular Radiotherapy and Therapeutic Innovation INSERM U1030 unit at Gustave Roussy Cancer Campus, Paris. This enthusiastic project in collaboration with the radiotherapy and pathology medical departments of Gustave Roussy aims to identify biomarkers for prediction of response to RT +/- immunotherapy based on Magnetic Resonance images. Topic: Cervical cancer is the 12th most common cancer in women in France. Clinical trials are in progress to validate the clinical benefit of the combination of radiotherapy (RT) + immunotherapy (IO) in locally advanced cervical cancers. This project aims at determining, based on retrospective and prospective data, a radiomics profile of response and resistance to RT +/- IO treatments based on magnetic resonance images. To do so, a radiomics signature assessing the amount of tumor-infiltrating CD8 T-cells based on MR images will be developed. In parallel, correlations between dynamics changes in radiomics features and immunological content for RT +/- IO treatments will be characterized with the goal to develop predictive signatures of local, loco-regional, distant recurrence-free and overall survivals. Optimizing the information generated by routine imaging performed during treatment will provide insights for candidate biomarkers, which will constitute valuable tools to be used in a personalized therapeutic approach.

We are looking for a highly motivated computing scientist with a strong academic and scientific background in Machine Learning and a real interest in medicine and oncology. Skills requested: – A PhD degree in Computer Science, Applied Mathematics or Electrical Engineering with an emphasis on Machine Learning – At least 3 years project experience in AI/ML/DL/MI (included PhD) – Good publication record – Excellent oral and written communication skills Work environment: The candidate will integrate a multidisciplinary team composed of physicists, physicians, engineers and biologists, with dedicated research in radiomics and artificial intelligence for RT +/- IO purposes. U1030 is part of Gustave Roussy Cancer Campus which is a world class cancer center leader in immunotherapy, precision medicine and cancer biology. The brachytherapy department is specialized in the management of gynecological cancers. It treats 200 patients with cervical cancer per year. It is particularly active in the development of combination strategies with targeted molecular therapies and immunotherapy (phases I and II). The department of Pathology has a strong expertise in gynecological pathology and has developed an experience in immuno-oncology.
Conditions of employment: the position is offered for 24 months, starting as soon as possible. Salary: depending on experience

Apply: applicants should send a cover letter summarizing past experiences and research interests, a CV including a list of publications, and contact information for two references to Dr Charlotte Robert, Associate Professor at Paris Saclay University.

Date limite de candidature: 2020-09-15
Mail de contact:
Intitulé: Deep Learning on histopathological images to identify biomarkers of response to immunotherapy
Type d’offre d’emploi: Offre de post-doc
Détails de l’offre: Context: A postdoctoral position is available to work on the ARC Signi’t project entitled « Artificial intelligence-driven integration of Radiomics, Pathomics & Genomics to predict outcome of immunotherapy». This project is in collaboration of CentraleSupélec, Gustave Roussy and the startup TheraPanacea and it aims to identify biomarkers for prediction of response to immunotherapy. Topic: Recent advances in artificial intelligence have provided promising directions to assist physicians in diagnosis, prognosis, treatment decision, and assessment of outcomes. Radiomics driven signatures (Sun et al. Lancet Oncology 2018) have been proposed and have shown correlations with the response to anti-PD-(L)1 immunotherapy for various cancer locations and types paving the way to precision medicine. In this project, we aim to adopt an integrative/evidence-driven complex system approach for disease understanding through global reasoning on interrelated health variables such as genomics, radiomics and pathomics. The aim of ARC Signi’t project is to determine automatically the best combination of features towards optimal outcome predictions. Main duties and responsibilities: We are seeking to appoint a Postdoc candidate to join the project working on the digital pathology part. In particular, the candidate will conduct research on the topics of deep learning and medical imaging focusing on supervised and semi-supervised methods. The goal of the research will be the discovery of robust representations from Whole Slide pathological Imaging (WSI) that can serve as powerful biomarkers for response to immunotherapy. Moreover, the candidate will be involved on Msc and PhD students supervision. We are looking for a highly motivated computing scientist with a strong academic and scientific background in Deep Learning, Medical Imaging and a real interest in medicine and oncology. Skills requested: – A PhD degree in Computer Science, Applied Mathematics or Electrical Engineering with an emphasis on Machine Learning and Deep Learning – At least 3 years project experience in AI/ML/DL/MI (including PhD) – Good coding skills – Good publication record – Excellent oral and written communication skills Work environment: The candidate will be affiliated with CentraleSupélec a French Grande Ecole d’Ingénieurs, and the coordinating institution of the Graduate School of Engineering and System Sciences, at the University of Paris-Saclay, a new university created from the cluster of several top historical academic institutions in the South of Paris region. Moreover, the candidate will be integrated in a multidisciplinary team in Gustave Roussy composed of physicists, physicians and engineers. Gustave Roussy is one of the first cancer center in Europe and treats about 12,000 new patients per year. For a few years, Gustave Roussy has been working on different projects in collaboration with CentraleSupélec and TheraPanacea, a startup stemming from CentraleSupélec’s research laboratories, which is specialized in the design of intelligent software solutions in oncology and radiotherapy. Conditions of employment: the position is offered for 16 months, starting as soon as possible. Salary: depending on experience

Apply: applicants should send a cover letter summarizing past experience and research interests, a CV including a list of publications, and contact information for two references to Pr Eric Deutsch, chair of the radiation oncology department Gustave Roussy cancer campus and INSERM U1030 unit and Maria Vakalopoulou, Associate Professor at CentraleSupélec

Date limite de candidature: 2020-07-31
Mail de contact:
Intitulé: Multi-omics transfer learning to extend proteomics coverage
Type d’offre d’emploi: Offre de thèse
Détails de l’offre: Distinct genes are expressed in different cell types and under different conditions, yielding different proteins from cell to cell. Precisely measuring the dynamics of proteins (the « atoms of life ») would provide an unrivaled characterization of biological states. However, methodological obstacles currently impede robust and accurate estimation of protein abundance. On the one hand, the core technology of proteomics (namely mass spectrometry) is hampered by a complex missing data problem, with peptides (i.e., protein fragments being missed at random, while others are below the detection threshold. On the other hand, RNA-seq allows to robustly measure abundance of the whole transcriptome, with few missing data, but RNA abundance sometimes lacks correlation with protein abundance.

Objectives: Considering, we propose to integrate RNA-seq and mass spectrometry based proteomics. More precisely, and knowing transcription levels do not always reflect protein concentrations,the goal of this project will be to assess how well transcriptomic can help imputing quantitative proteomics data when peptides fall below the detection limit of the instrument.

Methodology: To achieve this goal, we propose the following roadmap:
1. Exploratory analysis of paired transcriptomic and proteomic samples. Preliminary analysis of datasets using standard pipelines and assessment of correlation levels between the two sets of data. Discrepancies between RNA and protein abundances have different sources: (1) not all RNAs are translated into proteins; (2) proteins and RNA have different half-lives; (3) some proteins are transported from other cell-types.
2. Develop a novel method to estimate protein abundance using jointly transcriptomic and proteomic data. Leverage the high quality information provided by the transcriptomic data to build a new predictor of protein abundance through the transfer learning / domain adaptation frame-work.
3. Facilitate reproducible and open science by sharing the method in a high quality open-source package.

Scientific environment
1. Within the Fundamental Research division of CEA Grenoble, the lab Exploring the Dynamics of Proteomes (EDyP - gathers multiple scientific areas of expertise (ranging from biology to applied mathematics) with the aim to develop analytical and computational methodsthat improve the proteome coverage of complex biological samples
2. The TIMC-IMAG - gathers scientists and clinicians towards the use of computer science and applied mathematics for understanding and controlling normal and pathological processes in biology and healthcare. Within the lab, the team BCM (Biologie Computationelle et Mathématique) focuses on developing data-driven and modeling methods for biology, living systems, and to better support our healthcare system.
3. This project will be financially supported by the artificial intelligence for high throughput biomedical investigations program of the Grenoble Multidisciplinary Institute for Artificial Intelligence (MIAI -, which fosters academic collaborations between Grenoble hospital, academic labs (among which TIMC-IMAG and EDyP), and artificial intelligence industry.

Profile: The profile sought is that of a graduate student (Master degree or equivalent) in Computer Science (Major in Artificial Intelligence, Data Science, or Bioinformatics) or in Applied Mathematics (Major in Signal Processing or Statistics) who has a strong interest in interdisciplinary work in biology. They must have programming skills (R or Python) and be fluent in either French or English. Applicants must send their CV to:
– Nelle Varoquaux, CNRS researcher, TIMC-IMAG:
– Thomas Burger, CNRS researcher, EDyP-lab:

Date limite de candidature: 2020-07-31
Mail de contact:
Intitulé: GAN et préimage sur graphes
Type d’offre d’emploi: Offre de thèse
Détails de l’offre: Preimage Problem for Graph Data
Keywords : Machine Learning, Graph Neural Networks, Generative Adversarial Networks, Preimage ProblemLocation: LITIS Laboratory (Rouen, France) in Normandy.
Supervisors :
– Benoit Gaüzère, LITIS, INSA Rouen∼bgauzere
– Paul Honeine, LITIS, University of Rouen
Start date : September 2020 or earlier
Duration : 36 monthsRequired skills
• Master in Applied Mathematics, Computer Science, Data Science, or equivalent
• Experience in Python programming
• Skills in graph theory, neural networks or graph-based methods constitute an advantage
Contact :
Required documents :
• Up-to-date CV
• A cover letter (research experience and interests)
• Recommendation letter or references

More information here :

Date limite de candidature: 2020-05-30
Mail de contact:
Intitulé: Graphes et transparence de l’intelligence artificielle via les systèmes de recommandation sociaux
Type d’offre d’emploi: Offre de stage
Détails de l’offre: Les systèmes de recommandation sont conçus pour faire correspondre les préférences d’utilisateur·ices avec des articles (contenus culturels et médiatiques, produits, etc.). Ces systèmes ont une trentaine d’années et des méthodes très différentes ont été successivement élaborées pour les rendre performants, de la factorisation de matrices au récent deep learning. Certains systèmes reposent sur une modélisation graphe, c’est-à-dire la modélisation des relations (u, i) entre un·e utilisateur·ice u et un contenu i.

Ces graphes, complexes, peuvent être unipartis ou bipartis, évoluer au cours du temps, s’organiser selon plusieurs couches, etc. La question de l’évaluation s’articule en général autour de la satisfaction de l’utilisateur·ice, quantifiée par une mesure de précision. Pourtant, c’est loin d’être le seul critère intéressant [2]. En particulier, le risque d’un système de recommandation trop proche du profil de l’utilisateur·ice est de l’enfermer autour d’une thématique donnée (bulle de filtre [3, 1]). La question de la diversité des recommandations d’un système est à creuser, tant à l’échelle individuelle (pour diversifier les recommandations d’une personne) que collective (pour couvrir l’ensemble des éléments du système). Par ailleurs, la possibilité d’expliquer les recommandations faites est cruciale dans de nombreux contextes. Il existe aujourd’hui une littérature large sur la diversité dans les systèmes de recommandation en général, mais celle-ci n’aborde que marginalement la question des graphes, alors même que leur versatilité n’est plus à prouver dans le domaine de l’analyse de données. Plusieurs travaux ont déjà démontré la faisabilité de cette approche [4], et il s’agit à présent d’aller plus loin.
Le but de ce stage est donc d’explorer la question de la diversité dans les systèmes de recommandation sociaux : est-ce qu’une telle modélisation, en permettant la visualisation immédiate d’un voisinage (et donc des contributions aux recommandations), est utilisable pour proposer des recommandations individuellement plus diverses? Qu’est-ce qui, dans la structure de graphe, permet de comprendre les recommandations effectuées? Comment peut-on (et veut-on) quantifier cette explicabilité?

Offre complète :

Date limite de candidature: 2019-03-01
Mail de contact:
Intitulé: Data Scientist – NLP
Type d’offre d’emploi: Offre de poste dans l’industrie
Détails de l’offre: voir :
Date limite de candidature: 2019-03-30
Mail de contact:
Intitulé: Assessment of intrusion detection systems (ML and robustness)
Type d’offre d’emploi: Offre de post-doc
Détails de l’offre: (english version follows)
Bonjour,dans le cadre du projet SPARTA (, nous offrons un poste de postdoc (F/H) d’un an
sur le sujet de l’évaluation des systèmes de détection d’intrusion et de gestion des événements de sécurité.
Le candidat développera une approche permettant d’évaluer la robustesse de ces systèmes via :
– la définition de métriques appropriées
– la conception d’une méthodologie d’évaluation comprenant des tests par injection de trafic générées automatiquement par des modèles génératifs et adverses
– la conception d’un modèle d’optimisation multi-objectifsNous recherchons un profil ayant soit :
– une thèse dans le domaine de l’apprentissage machine (apprentissage génératif, apprentissage adverse, sûreté de l’apprentissage) et des compétences en cybersécurité
– une thèse dans le domaine de la cybersécurité (détection d’intrusion, corrélation d’événement) et des compétences en apprentissage machinePlus de détails :



(english version)
Dear all,

in SPARTA project (, we are offering a postdoc position (F/M) for one year
on the topic of intrusion detection systems and security information and event management systems assessment.
The candidate will develop an approach enabling the assessment of the robustness of such systems through:
– the definition of appropriate metrics
– the design of an assessment methodology including pratical traffic injection tests, generated automatically using generative and adversarial models
– the design of a multi-objective optimisation model

We are looking for the following profile:
– EITHER a Ph.D in machine learning (generative learning, adversarial learning, reliability of ML) with competences in cybersecurity
– OR a Ph.D in cybersecurity (intrusion detection, alert correlation) with competences in machine learning

More details:

Best regards,


Date limite de candidature: 2020-03-13
Mail de contact:
Intitulé: Postes Enseignant-Chercheur ENSEA Cergy
Type d’offre d’emploi: Offre de poste dans l’académique
Détails de l’offre: L’ENSEA ( recrute deux postes d’enseignant-chercheur pour la rentrée 2020 dans le domaine de l’IA (Machine Learning) pour la science des données massives et multimodales.Un poste de Professeur des Universités 27/61 :

Un poste de Maître de conférences 61/27 :

Laboratoire de rattachement : ETIS, UMR CNRS 8051 (
Se référer aux profils des postes pour les contacts enseignement et recherche.

Date limite de candidature: 2020-03-26
Mail de contact:
Intitulé: Privacy in embedding-based neural networks by means of homomorphic encryption
Type d’offre d’emploi: Offre de post-doc
Détails de l’offre: AI presently emerges as the killer application of homomorphic encryption or FHE. Indeed, this kind of cryptography, which allows to perform general calculations directly over encrypted data, has the potential of bringing privacy-by-construction for either or both user or model data, depending on the application scenario. In the longer term, FHE may also help protect training data, unleashing new usages in training data sharing and collaborative AI model building. In this context, the present postdoctoral offer aims at investigating the practical relevance of homomorphic encryption in the case of a specific kind of neural networks, the so-called embedding-based networks, which, for intrinsic reasons, both are favorable to good homomorphic execution performances and enjoy a wide spectrum of applications. Thus, this postdoctorate will study the theoretical and practical aspects cropping up in several FHE integration scenarios with this kind of neural nets and will also lead to prototyping work on a best-in-class open-source speech recognition system which ifself uses an embedding-based network.
Date limite de candidature: 2020-03-31
Tél de contact: 01 69 08 45 42
Mail de contact:
Intitulé: Deep Learning pour un assistant culinaire
Type d’offre d’emploi: Offre de post-doc
Détails de l’offre: Mots-clés : Apprentissage, Système de recommandation, Deep Learning (Embedding,
Auto-encodeur, GAN, etc.)Laboratoire d’accueil : LIRIS –
Durée du contrat : 12 mois
Rémunération (2400 € mensuel)
Date de début : début 2020
Candidature : CV et lettre/mail de motivation à envoyer frederic.armetta [at]

Le post-doc proposé intervient dans le cadre d’un transfert technologique pour
une entreprise développant un assistant culinaire. Différents datasets de
recettes de cuisine ont récemment été rendus disponibles et permettent
l’apprentissage de réseaux de neurones profonds (voir par exemple [1]). Dans le
cadre de ce projet, on s’intéresse à proposer des aménagement de recettes de
cuisines suivant les ingrédients disponibles (réparation de recettes).
Le post-doctorant aura pour tâche
* Organiser différents jeux de données à des fin d’apprentissage
* Expérimenter, évaluer et combiner les architectures les plus pertinentes pour
un système de recommandation culinaire.
* Une ou plusieurs publications des travaux réalisés

Profil recherché pour le poste :
* Doctorat en Informatique
* Spécialisé dans le domaine de Deep Learning
* Familiarisé avec les frameworks les plus courants (keras, pytorch, tensorflow,

[1] Generating Personalized Recipes from Historical User Preferences,
Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, Julian McAuley,, 2019
Date limite de candidature: 2020-01-20
Tél de contact:
Mail de contact:
Intitulé: 4 offres de postdoc en Apprentissage/IA à Télécom Paris, Institut Polytechnique de Paris
Type d’offre d’emploi: Offre de post-doc
Détails de l’offre: We are currently seeking 4 postdoctoral positions to the join Department Image, Data and Signal of Télécom Paris, within the newly created Institut Polytechnique de Paris (France), to work on Machine Learning and Artificial Intelligence.
Websites: and
Positions description:
A – a 3-year postdoctoral position in Machine Learning in Statistics, Signal and Machine Learning group.
Role: Develop groundbreaking research in the field of theoretical or applied machine learning, targeting applications that are well aligned with the topics of the S²A group [3] and the Images, Data & Signals department, which include (and is not restricted to) sequential/reinforcement learning, multitask learning, learning for structured data (e.g. time series analysis, audio signals), natural language processing, social signal processing, predictive maintenance, biomedical or physiological signal analysis, recommendation, finance, health, …. – Participate to academic and industrial collaborations on the same topic – Participate to teaching activities at Telecom Paris in machine learning and Data science, including continued-education programs (e.g. the Data Scientist and IA certificates/specialized masters) . Contact: Stephan Clémençon, head of S2A group. Stephan.clemencon {at]
B- a 3-year and a 2-year postdoctoral positions in Machine Learning with the Data Science and Artificial Intelligence for Digitalized Industry and Serviceson one of the following topics :
Robustness and Interpretability for Trusted Machine Learning, Deep Kernel Machines , Zero-Shot Learning/Transfer Learning, Autonomous learning/lifelong learning.Role: In the context of the Chair DSAIDIS, conduct groundbreaking researches on the cited topics with the team. Participate to the supervision of PhD students. Publish in the main top-ranked international conferences in Machine Learning. Actively contribute to the scientific animation of the Chair and foster collaborations between the team and the industrial partners
Contact: Florence d’Alché, florence.dalche {at], website :
C – a 2-years postdoctoral position within an academic/industry collaboration focused on online anomaly detection in the context of manufacturing lines.
Role: The postdoc will lead research works on online anomaly detection methods able to handle heterogeneous data collected from a manufacturing line in quasi-real time. The new methods will be applied on a real large scale dataset in close collaboration with an industrial group. The postdoc will contribute to the deliverables of the project and will collaborate with a PhD student and the team already active on the project.
Contact: Florence d’Alché and Pavlo Mozharovskyi, pavlo.mozharovskyi {at]
For all positions: 
a PhD in machine learning, statistics/biostatistics, computer science
a strong publishing experience in peer-reviewed journals and top-tier conferences in Machine Learning, an excellent background in applied mathematics/statistics. Strong interest for computational aspects of Machine learning is expected as very good skills in programming (Python). Excellent skills in English.
Location: Campus of Institut Polytechnique de Paris: Télécom Paris, 19 place Marguerite Perey, F-91120 Palaiseau
To apply, please send an email to the contact persons
with the sentence “Postdoc application” as a subject with a mention A, B or C, depending of the targeted position
with a single file containing a statement of research interests, a CV, a copy of relevant certificates, (p)reprints of two publications, and a list of two references.
candidatures will be considered as soon as they arrive and at the latest, November 20.
positions can start from November 20 and will be open till they are filled.
Date limite de candidature: 2019-11-20
Mail de contact:
Intitulé: Transfer Learning for Graph mining
Type d’offre d’emploi: Offre de post-doc
Détails de l’offre: Applications are invited for a 12-month-postdoctoral fellowship in Machine Learning/Data Mining at Université Jean Monnet Saint-Etienne, within the Data Intelligence team of the Laboratoire Hubert Curien
The position is funded by IDEX Lyon IMPULSIONLocation: Hubert Curien Laboratory UMR 5516, Saint-Etienne
Keywords: Machine Learning, Graph Mining, Representation Learning, Transfer Learning
Description: Representation learning for graph mining is a significant challenge. Data in the form of graphs has become ubiquitous for describing complex information or structures. In the social sciences, graphs will make it possible to study relationships and interactions between people; in biology, we will focus on graphs to model genetic interactions or metabolic networks. However, compared to images or text, the structure of a graph is very irregular making learning a good representation more difficult. This post-doctorate is articulated around 2 research axes. The first direction is to look at representation transfer for graph mining. In this context, the objective will be to work on learning representations which are, on the one hand, specific to the tasks that one wishes to solve, for example the prediction of link, the classification of nodes or the detection of communities, but also exploit the potential dependencies between these different tasks. This first axis naturally opens the way to the processing of dynamic graphs. In this case, the graphs are most often represented as a collection of static graphs at different times, and for each graph a new representation is systematically learned. The objective is to develop a learning framework capable of detecting significant changes in the structure of the same graph, and to adapt the representation learned so as to transfer the immutable characteristics and thus to preserve the knowledge already acquired.
Required profile: The candidate has a Ph.D. and will have a strong background in machine learning including a good foundation in statistical learning and mathematics. He is also expected to have a good level in programming. Finally, the candidate must also have a good level of English and have both an interest in theoretical and practical aspects.
Supervisors:Charlotte Laclau,
Baptiste Jeudy,
Date limite de candidature: 2019-11-15
Mail de contact:
Intitulé: Apprentissage profond non supervisé de représentations spatio-temporelles pour la vidéo
Type d’offre d’emploi: Offre de thèse
Détails de l’offre: Identifier des actions ou une succession d’événements en fonction de l’expérience est une partie importante du processus de prise de décision humaine. Simuler ce processus par des machines en leur apprenant à localiser et identifier les événements en se basant sur des représentations internes de l’environnement pourrait être utile pour de nombreuses tâches de l’analyse vidéo telles que la reconnaissance, la détection et le suivi d’objets.
Les récents progrès dans de l’apprentissage profond et l’augmentation de la puissance de calcul des GPU spécialisés permettent d’envisager des architectures répondant à cette problématique. Cependant l’apprentissage profond supervisé nécessite un volume considérable de données étiquetées afin d’obtenir des résultats pertinents. Dans le domaine de la vidéo, rares sont les acteurs qui disposent d’un tel volume de données étiquetées.
Utilisant des vidéos non-étiquetées, nous souhaitons apprendre de manière non-supervisée un réseau profond encodant des représentations pour les vidéos. L’objectif est de capturer la nature spatio-temporelle des vidéos dans un modèle génératif efficace, et non de traiter indépendamment les dimensions spatiales (images) et la dimension temporelle. Au même titre que les premières couches des réseaux convolutionnels 2D encodent des descripteurs locaux spécialisés pour les images, nous souhaitons apprendre des descripteurs spatio-temporels permettant des modéliser les vidéos.
Date limite de candidature: 2019-09-30
Tél de contact: 04 27 11 80 30
Mail de contact:
Intitulé: Machine learning and genomics: multi-locus genome-wide association studies
Type d’offre d’emploi: Offre de post-doc
Détails de l’offre: Dans le cadre de mon projet ANR SCAPHE, je suis à la recherche d’un.e docteur.e en machine learning ou statistiques pour développer des modèles de sélection de variables en haute dimension appliquées aux études d’association pangénomiques.Les candidatures seront examinées au fil de l’eau. Le contrat débutera en janvier 2020 au plus tard.

Pour plus de détails :

Date limite de candidature: 2019-07-31T
Mail de contact:
Intitulé: Poste de Chargé de Recherche en IA pour la Robotique à MINES ParisTech
Type d’offre d’emploi: Offre de poste dans l’académique
Détails de l’offre: Senior Scientist in Computational Biology: CBIO (Mines ParisTech)Institution: MINES ParisTech (Ecole Nationale Supérieure des Mines de Paris), PSL University
Research Center: Centre for Computational Biology (CBIO)

Centre for Computational Biology (CBIO):
The Centre for Computational Biology is part of Mines ParisTech, one of the major engineering schools for applied mathematics in France. Our research is dedicated to the development of methods and tools in the field of Machine Learning, Statistics and Computer Vision in order to analyze massive data generated in life sciences and medicine. We work on a broad range of applications, from questions in fundamental life science to precision medicine. The Centre for Computational Biology has a partnership with INSERM, the French national health institute, and the Institut Curie, a major hospital and research center dedicated to cancer. This partnership provides access to the infrastructure and facilities of the Institut Curie and facilitates collaborative projects with other groups at the Institut Curie, as well as data sharing. Our laboratory is located in the heart of Paris and we therefore benefit from an exceptional scientific and cultural environment.

Position summary:
We are seeking a senior scientist with several years of experience as an independent researcher and with an excellent track record in the fields of Machine Learning and Statistics applied to biology and/or medicine. In particular, we would like to consolidate our activity in the fields of genomics and analysis of next-generation sequencing data with applications both in fundamental biology and medicine. Any previous experience in these fields would be an advantage. The successful candidate is expected to work in a team of 4 permanent researchers on exciting questions in computational biology, to develop his/her own line of research for which (s)he will secure funding, to supervise PhD students and Postdocs and to contribute to the scientific animation in his field of research at the CBIO and the Institut Curie.

Application file:
The application should consist of the following documents:
– a cover letter;
– a detailed CV;
– a research project (5 pages limit);
– a list of publications and of oral communications at scientific conferences;
– three recommendation letters to be sent directly to the CBIO. Alternatively, the candidate can provide contact details for three referees to be contacted directly for recommendation;
– Copies of official transcripts for all degrees at all institutions attended.
Please send the documents before 06/06/2019, to Thomas Walter, by email (, or by mail (CBIO, Mines ParisTech, 60 Boulevard Saint Michel, 75006 Paris).

Date limite de candidature: 2019-06-06
Tél de contact: 0140519024
Détails de l’offre: Le Centre de Robotique ( de MINES ParisTech (situé au 60 Bd St-Michel 75006) recrute un Chargé de Recherche (CDI de droit public) en Intelligence Artificielle pour la Robotique et les Véhicules Intelligents.Ce poste s’adresse à un jeune chercheur (H/F, idéalement 3-10 ans après la thèse ) ayant le goût d’un travail multidisciplinaire à l’interface de la recherche fondamentale et du monde industriel, *dans le domaine des Véhicules Autonomes et de la Robotique*.

Profil recherché : titulaire d’une thèse idéalement dans le domaine du Deep-Learning pour la Robotique ou le Véhicule Autonome (ou sinon, soit thèse en Deep-Learning appliqué à un autre domaine, soit thèse dans le domaine Perception ou Planification en Robotique ou Véhicule Autonome), et idéalement au moins une 1ere expérience post-thèse dans ces mêmes domaines [mais tenure-track juste après thèse aussi envisageable].

Pour + de détails sur le laboratoire, la fiche de poste et les modalités de candidature, voir la page ci-dessous :

Date limite de candidature: 2019-05-06
Mail de contact:
Intitulé: Postdoctoral position in Machine Learning for Opinion Mining in Social Networks
Type d’offre d’emploi: Offre de post-doc
Détails de l’offre: The App ( and MIND ( teams of the LITIS lab at INSA Rouen Normandy offer a postdoctoral position for 12 months as part of the SAPhIRS project.Keywords: machine learning, deep learning, recurrent neural networks

Description of the project and postdoctoral missions:

Social networks are regularly used to express opinions on public and political events or to disseminate opinions on sensitive topics (hate speech, hooliganism, racism and nationalism, etc.). The objective of the SAPhIRS project is to study opinion propagation within social networks: to identify the key mechanisms for disseminating information and opinion and to identify leaders of influence. Particularly in Security field, we will focus on Twitter detection and analysis of hamessages calling for hatred or violence, monitoring their spread and detection of influential actors.

As part of this project, we propose a 12-month postdoctoral position in machine learning for opinion mining, sentiment analysis and the detection of changes of opinion in Tweets. For this purpose, we plan to use state-of-the-art methods in NLP based on deep-learning neural networks, and especially recurrent neural networks with internal memory such as LSTMs or GRUs.

In other words, the main tasks would be:

To annotate tweets automatically according to an opinion: supervised classification problem;
To automatically identify messages containing the expression of radical ideas, in English, in French and in Arabic chat alphabet (transliteration of Arabic in Latin alphabet, also called arabizi or arabish): problem of supervised learning on unbalanced classes and possibly weakly supervised learning;
To detect changes of opinion in user Tweets sequences: detection of anomalies and breaks in a time series.

The main difficulties come from the encoding of input data (short texts from Twitter, in French and Arabizi) for which language models remain to be defined, and in the design and learning of adapted recurrent models dedicated to these three tasks.

Candidates must have a doctorate in Machine Learning with, if possible, experience in NLP and/or Deep Learning. Knowledge of recurrent networks and Arabizi would also be a plus.

Contractual conditions:
The contract will be 12 months starting as soon as possible, with a gross salary of approximately 3500 €. The recruited person will work in the LITIS lab at INSA Rouen Normandy in Saint-Etienne-du-Rouvray.

Application: CV, motivation letter, recommendation letters.

Date limite de candidature: 2019-09-01
Mail de contact: