Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Université Aix-Marseille
Durée : 2 ans
Contact : Paul.villoutreix@univ-amu.fr
Date limite de publication : 2022-01-01

Contexte :
Recent years have witnessed an explosion of data in biology and medicine. Many acquisition techniques, like microscopy or sequencing techniques, provide complementary views of the same system, e.g. an organ, an embryo, a tumor. To understand the dynamics happening at single cell resolution and develop new personalized treatments, we need to integrate these complementary sources of information. To tackle this problem, this project aims at developing new Temporal Data Integration theoretical and computational methods for various complementary acquisition techniques (microscopy, and multi-omics).

Sujet :
When studying a biological system such as a developing embryo, many acquisition techniques are available. Each of them brings out unique features of the system, however, they are often incompatible and cannot be performed at the same time. To address this challenge we need to develop multi-domain integration techniques. Current approaches rely either on the tools of optimal transport, or multiple autoencoders, however, they are not designed to address temporal data. With this project, we propose to take advantage of multi-domain dynamical data in high-dimensional spaces to infer a dynamical coupling between sequencing data acquisition techniques (such as sc-RNASeq) and microscopy data. This will include theoretical work and computational experiments on artificial and real data. The results of the project are expected to have large impact in the machine learning community and be of wide applicability in real world biological problems. The scientific environment for this project is ideal as it combines expertise in interdisciplinary approaches of machine learning applied to biological data, and expertise in theoretical machine learning.

Profil du candidat :
We are looking for a PhD in machine learning, computer science, applied mathematics with strong interest in machine learning and its applications to biology. The postdoc will take place in Paul Villoutreix’s interdisciplinary team (Learning meaningful representation of life http://bioml.lis-lab.fr/) and the Machine Learning team of the Computer Science lab in Marseille (https://qarma.lis-lab.fr).

Formation et compétences requises :
Theoretical background and coding skills in Machine Learning.

Adresse d’emploi :
Turing Center for Living Systems – CENTURI (Marseille)
LIS and Université Aix Marseille

Document attaché : 202109090700_postdoc temporal data integration.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : any CNRS laboratory
Durée : permanent, tenured p
Contact : jan.stark@l2it.in2p3.fr
Date limite de publication : 2022-01-11

Contexte :
One of the novelties at this year’s edition of the “concours chercheurs CNRS”,

https://www.dgdr.cnrs.fr/drhchercheurs/concoursch/default-en.htm

is the availability of five permanent positions as junior full-time researchers in the newly created interdisciplinary section “sciences and data”. They can be found at the very end of the list of positions on the website quoted above (“interdisciplinary committee no. 55”). The “concours chercheurs” is a competitive recruitment process used by CNRS to hire its researchers.

None of the five positions is attached to a given CNRS laboratory, i.e. candidates are free to propose a research project with any CNRS laboratory. One of the five positions is on the subject

“Advanced methods of artificial intelligence for the processing, reconstruction and analysis of ATLAS experiment data at the Large Hadron Collider”.

Are you a young colleague with demonstrated experience in the field of advanced artificial intelligence? Are you interested in applications to particle physics, but do not have much/any hands-on experience with physics? Please do not hesitate to get in touch with members of the ATLAS team at the “Laboratoire des 2 Infinis – Toulouse” (L2IT). We are happy to discuss ATLAS and the LHC with you.

One of our recent conference proceedings on the use of artificial intelligence for ATLAS and the LHC can be found here:

https://www.epj-conferences.org/articles/epjconf/abs/2021/05/epjconf_chep2021_03047/epjconf_chep2021_03047.html

More general information on L2IT, a new laboratory created in January 2020, can be found here:

https://indico.in2p3.fr/event/24978/

Sujet :
–

Profil du candidat :
–

Formation et compétences requises :
–

Adresse d’emploi :
–

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LIPN, UMR CNRS 7030
Durée : 18 mois
Contact : mustapha.lebbah@univ-paris13.fr
Date limite de publication : 2022-01-31

Contexte :
https://bit.ly/3s1fB6X

Sujet :
https://bit.ly/3s1fB6X

Profil du candidat :
PhD en apprentissage statistique ou en informatique (data science).
Solide bases en statistique (modèle de mélange) et en informatique.
Maîtrise de la programmation en Python, Java/Scala
Connaissances de Git, Docker, Environnements Cloud, Calcul distribué sur des clusters
Connaissances en génie logiciel
Esprit de synthèse.
Créativité, force de proposition.

Formation et compétences requises :
https://bit.ly/3s1fB6X

Adresse d’emploi :
LIPN, UMR CNRS 7030
Université Paris XIII – USPN

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Météo-France research department (CNRM), Toulouse,
Durée : 21 to 27 months
Contact : laure.raynaud@meteo.fr
Date limite de publication : 2022-01-31

Contexte :
High-Impact Weather (HIW) events have devastating effects on society, causing human losses, degradation of infrastructures and large economic impacts. Severe precipitating events, damaging thunderstorms and strong winds are among the most impacting events from a meteorological point of view, with various severe indirect effects such as flooding, landslides and marine submersion. Being rare, HIW events lie in the tail of climatological distribution of weather events. Although meteorological services such as Météo-France have done significant progress in predicting weather for the last decades, accurately predicting the occurrence, intensity, location and timing of HIW still remains challenging.
Currently operational weather forecasts rely on physically-based modelling approaches, and Numerical Weather Prediction (NWP) models are operated daily to determine the future atmospheric states and the risk of HIW. In particular, Ensemble prediction systems (EPSs) aim at sampling the probability distribution of future atmospheric states. They consist in running several NWP forecasts in order to account for the different sources of uncertainty. At Météo-France, the operational AROME-EPS, which runs 16 perturbed forecasts with a spatial resolution of 1.3km, is used to anticipate the risk of HIW. However, properly capturing the associated uncertainty requires very high resolution (few hundred meters) large-size (few hundred members) ensembles. Nonetheless, such enhanced systems are currently unfeasible for operational NWP because of the associated computational cost.
In this context, the main objective of the POESY project is to explore the scientific feasibility and relevance of an innovative hybrid EPS design, combining standard physical modelling with computationally-efficient Artificial Intelligence (AI) techniques, in order to produce disruptive probabilistic forecasts for high-impact weather.

Sujet :
The goal of this post-doctoral position is to improve the representation of forecast probability distributions by increasing the AROME-EPS sampling from O(10) to 0(1000) forecasts thanks to complementary AI-generated forecasts. For that purpose, physically-constrained deep generative models such as GANs and Variational AutoEncoders will be developed and evaluated. Besombes et al. (2021) provides a first example of GAN-based weather scenario. A crucial part of the work will be to adapt off-the-shelf learning architectures to the particularities of this geophysical problem. A specific attention will be paid to the following points : the learning of extremes, ensuring spatial, temporal and physical consistencies in the generated forecasts, mode collapse problem.

Profil du candidat :
The ideal candidate would have the following qualifications :
– a strong background in deep learning algorithms, in particular convolutional neural networks and deep generative models
– experience in geophysical problems would be appreciated, at least a strong interest for applied research in atmopsheric physics is highly recommended
– Proficiency with Python programming and AI librairies
– Experience with processing large volumes of data
– Experience of working in a Linux-based environment
– Aptitude for scientific work, written and oral communication in English, meetings abroad possible
– A scientific curiosity, autonomy, rigor in the interpretation of the results

Formation et compétences requises :
PhD degree in atmopsheric sciences, statistics or artificial intelligence.

Adresse d’emploi :
Toulouse, France.

Document attaché : 202112101209_PostdocPOESY.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : ICube, University of Strasbourg
Durée : 24 months
Contact : lampert@unistra.fr
Date limite de publication : 2022-01-31

Contexte :
A Postdoc position is open at University of Strasbourg (ICube Laboratory) – France

Deep Learning, Domain Adaptation, Multi-Modal Representations
The position will be funded for two years (initially for one year, renewable for an additional year). The candidate will join the SDC research team under the supervision of Dr Thomas Lampert, the Chair of Data Science and Artificial Intelligence, and join his international team of PhD students and engineer to develop novel deep learning approaches to domain invariant representation learning (particularly in multi-modal data), with application (but not restricted) to Medical Imaging and Remote Sensing. The funding is not connected to a particular project, so it is the perfect opportunity for a strong candidate to explore new directions under the supervision of the Chair.

Sujet :
Send a letter of motivation, your CV, and an example publication to Thomas Lamper and Gisèle Burgart (l1ampert@uni2stra.fr andg1burgart@uni2stra.fr – !remove the numbers!) with the subject beginning with [Chaire Postdoc].

The position will remain open until a suitable candidate is found and the starting date will be agreed upon with the successful candidate (but can start ASAP).

Detailed Description: https://seafile.unistra.fr/f/5931f91dcffb401db566/?dl=1

Profil du candidat :
The successful candidate will have (or will soon obtain) a PhD in computer science or related domain and have experience in deep learning and applied machine learning and a strong level of written and spoken English. Experience with transformers, GANs, autoencoders, and/or unsupervised/self-supervised DL (autoencoders, etc) would be a plus. You will join a growing team and will have the freedom to follow your interests in a direction complementary to the abovementioned research focusses. You will be expected to target leading outlets in the field of machine learning and have a strong track record of publications. Candidates who are able to carry out the highest quality research independently, to co-supervise PhD students, and to give their input on a number of projects being carried out in the team are pursued. You will have access to state-of-the-art hardware for deep learning.

Formation et compétences requises :
M2 Informatique

Adresse d’emploi :
ICube, University of Strasbourg

Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : EPIONE group – Inria Sophia Antipolis
Durée : 18 months
Contact : marco.lorenzi@inria.fr
Date limite de publication : 2022-01-31

Contexte :
The project Fed-BioMed focuses on methodological and technical advances towards the development of a novel generation of federated learning methods for the analysis of private and large-scale multi-centric biomedical data. The project has a specific focus on the efficient federation of frameworks robust to data heterogeneity and uncertainty, and tackles the following scientific challenges:

– Methodological. Extending the federated paradigm to novel scalable approaches to probabilistic modeling and prediction from siloed data.
– Technical. Developing our federated learning framework through a self-contained system that can be securely deployed across different centers and collaborators (fedbiomed.gitlabpages.inria.fr).
– Translational. Demonstrating federated learning on two applications: 1) Discovering novel genetic underpinnings of neurological and psychiatric disorders, and 2) Prediction of response to immunotherapy from the analysis of federated lung imaging data.

Sujet :
During the project the candidate will:

• Develop learning methods for federated analysis for private and distributed data;
• Deploy advanced statistical learning methods into a wide range of biomedical/clinical applications;
• Interact with INRIA researchers and engineers, and participate to the scientific life of the team;

Profil du candidat :
We look for a motivated candidate holding a PhD in a domain among computer science, biomedical engineering, and related fields.
A proven track record of publications and presentations to scientific events is required.

Formation et compétences requises :
Demonstrable experience in some of the following topics (the more the better):

– Statistics, Bayesian Modeling;
– Optimization, Distributed Computing;
– Python and PyTorch/TensorFlow;
– Biomedical Data Analysis;
– Signal Processing;

Strong communication abilities are necessary, as well as motivation in taking responsibilities (e.g. supervision, organization of scientific events).

Adresse d’emploi :
Epione team (Inria), located in the tech park of Sophia Antipolis (France).
Email: marco.lorenzi@inria.fr

Document attaché : 202111220859_job_offer-PostDoc-FedBioMED_v2.pdf

Offre en lien avec l’Action/le Réseau : SimpleText/– — –

Laboratoire/Entreprise : Observatoire Astronomique de Strasbourg UMR 7550
Durée : 12 mois
Contact : gilles.landais@astro.unistra.fr
Date limite de publication : 2022-01-31

Contexte :
L’annonce du poste est publiée via les offres d’emploi du CNRS – https://bit.ly/3IskUCz

Poste de développeur au Centre de données de Strasbourg (CDS) sur le service de catalogue VizieR (https://vizier.cds.unistra.fr).

Le CDS est hébergé dans L’Observatoire Astronomique de Strasbourg,
il regroupe une trentaine de personnes : des astronomes, des documentalistes et des ingénieurs qui fournissent des services dont les données sont libre d’accès et sont largement utilisées dans la communauté (https://cds.unistra.fr/).

————————————————————————————————
(english version)
The job announcement is published via CNRS job offers – https://bit.ly/3IskUCz

Developer position at the Strasbourg Data Centre (CDS) on the VizieR catalogue service (https://vizier.cds.unistra.fr).

The CDS is hosted in the Observatoire Astronomique de Strasbourg,
It gathers about 38 people: astronomers, documentalists and engineers who provide services whose data are freely accessible and widely used in the community (https://cds.unistra.fr/).

Sujet :
Le Centre de Données astronomiques de Strasbourg (CDS) recherche un-e développeur(euse) pour faire évoluer le Système d’Information de publication des données en astronomie. Il/elle viendra renforcer l’équipe d’ingénieur(e)s informaticien(ne)s en charge du service Vizier pour faire évoluer les workflows qui mettent en base de données les tables publiées dans les journaux ou missions spatiales. Les développements incluront l’évolution de l’indexation des données temporelles et spatiales.

———————————————————————————————————-
(english version)
The Astronomy Data Centre of Strasbourg (CDS) is looking for a software engineer to work on the Astronomy Data Publication Information System. He/she will reinforce the team of computer engineers in charge of the Vizier service in order to develop the workflows that put in the database the tables published in the journals or space missions. The developments will include the evolution of the system for the indexing of temporal and spatial data.

Profil du candidat :
Nous recherchons un développeur de logiciels pour travailler sur le service de catalogue VizieR qui est utilisé par les astronomes du monde entier.

————————————————————————————————————
(english version)
We are looking for a software developer to work on the VizieR catalogue service which is used by astronomers all over the world.

Formation et compétences requises :
– Maîtrise des développements C, Python en environnement Linux
– Bonne Connaissances en bases de données relationnelles (PostgreSQL) et du langage SQL
– La connaissance d’un autre langage comme Java ou Rust serait un plus
– Une expérience de création d’un analyseur syntaxique serait utile et appréciée
– Anglais niveau B1 minimum
– Capacité à présenter son travail (collaborations techniques en anglais dans le cadre de projets internationaux)
– Autonomie, qualités relationnelles, sens de l’organisation, capacités d’adaptation et des aptitudes à travailler en interaction avec une ou plusieurs équipes
– Le/la candidat(e) aura un diplôme d’école d’ingénieur (ou équivalent)

———————————————————————————————————-
(english version)
– Expertise in C or Python developments in Linux environment
– Good knowledge of relational databases (PostgreSQL) and SQL language
– Knowledge of another language such as Java or Rust would be a plus
– Experience in creating a parser would be useful and appreciated
– English level B1 minimum
– Ability to present your work (technical collaborations in English in the context of international projects)
– Autonomy, interpersonal skills, organisational skills, adaptability and ability to work in interaction with one or more teams
– The candidate should have a diploma from an engineering school (or equivalent)

Adresse d’emploi :
Observatoire Astronomique de Strasbourg
11, rue de l’université
67000 Strasbourg

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : IRIT
Durée : 6 mois
Contact : pascal.dayre@irit.fr
Date limite de publication : 2022-02-01

Contexte :
le projet ANR SO-DRIIHM [1] recrute un ingénieur d’étude pour 6 mois à partir du 1er décembre.

Dans la continuité de l’opendata, le courant de la science ouverte a pour objectif de faciliter le partage des ressources et des productions scientifiques afin de rendre la science plus éthique. Une science plus transparente, plus reproductible et plus additionnelle.

Cet emploi se fera dans un contexte riche de plusieurs projets autour de la science ouverte, des données et de l’IA. Des échanges avec la communauté d’usage du labex DRIIHM et avec un partenaire industriel auront lieu. Les développements se feront selon l’état de l’art des développements internet et des standards liés aux données ouvertes.

Sujet :
CDD Ingénieur d’étude développement 6 mois / Toulouse – python / backend Django / OpenAPI / web sémantique / sparQL

Il s’agit de travailler sur le développement du serveur d’une plateforme internet pour la science ouverte.

La partie serveur de la plateforme s’appuient sur le framework python Django et ses déclinaisons. La persistance des données est assurée par un système de gestion de bases de données sémantiques (triplestore RDF/RDFS). Le langage de requêtage sparQL, extension du SQL est utilisé. La plateforme est en interaction avec un ensemble de services de l’éco-système numérique de la science ouverte.

Pour plus d’information, vous pouvez consulter l’offre d’emploi
https://emploi.cnrs.fr/Offres/CDD/UMR5602-EMILER-004/Default.aspx

Profil du candidat :
Bac + 5 ans et première expérience souhaités.

Ingénieur en informtique

Formation et compétences requises :
Développement informatique.

– Bonne connaissance de python et de Django
– Bonne connaissance de l’algorithmique
– Connaissance du développement orienté objet
– Connaître Gitlab, Github voire Docker
– Connaissance de l’environnement de développement python et d’un framework (Django)
– Connaissance de l’IDE Visual code studio
– Connaissance des architectures des systèmes d’information sur internet
– Rédiger et mettre à jour la documentation fonctionnelle et technique (wiki, Markdown voire latex)
– Connaissance d’UML pour mettre à jour la documentation technique
– Travailler en équipe
– Élaborer et mettre en œuvre un plan de test
– Connaissance système et linux : connaissance d’administration d’un système linux bienvenue (ubuntu 20.04)
– Connaissance des outils et standards du web sémantique : RDF et les bases de graphe, langage de requête SPARQL, XML, RDF, OWL, Triplestore, DCAT
– Connaissance du web des données, des bases de données sémantiques et de sparQL. Construire et administrer des bases de données graphes (triplestore graphDB sparQL)
– Connaissance d’un framework javascript est un plus pour bien comprendre le besoin et potentiellement intervenir ponctuellement sur le code du client

Adresse d’emploi :
Le lieu du CDD est l’IRIT (Institut de Recherche en Informatique de Toulouse – site UPS)

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Observatoire Astronomique de Strasbourg
Durée : 12 mois (renouvelabl
Contact : soizick.lesteven@astro.unistra.fr
Date limite de publication : 2022-02-10

Contexte :
Le Centre de Données astronomiques de Strasbourg (CDS) gère d’importantes bases de données qui servent de référence pour la communauté astronomique internationale. Le CDS développe des services en ligne qui permettent d’accéder à ces bases et aussi de les exploiter (http://cds.unistra.fr/). Le CDS compte parmi ses utilisateurs les agences spatiales (l’Agence Spatiale Européenne (ESA) et la NASA), les principaux observatoires, tel que l’Observatoire Austral Européen (ESO), ainsi que l’ensemble des instituts de recherche en astrophysique possédant une composante observationnelle.VizieR, en particulier, est une base de données de catalogues du ciel, qui traite plus de 500 000 requêtes par jour, reçues du monde entier.

——————————————————————————————————-
(english version)
The Strasbourg Astronomical Data Centre (CDS) provides scientific services that serve as a reference for the international astronomical community. The CDS develops online services that allow access to these databases and also to exploit them (http://cds.unistra.fr/). CDS users include space agencies (the European Space Agency (ESA) and NASA), major observatories such as the European Southern Observatory (ESO), as well as all astrophysical research institutes with an observational component. VizieR, in particular, is a service for astronomical catalogues which is heavily used by the community with more than 500,000 queries per day, received from all over the world.

Sujet :
Au sein d’une équipe de documentalistes (Chargés du traitement des données scientifiques), d’astronomes et d’informaticiens, vous serez chargé(e) de décrire, indexer, standardiser et vérifier des données tabulaires provenant des principales revues d’astrophysique pour alimenter la base de données VizieR, dans le but d’optimiser leur exploitation scientifique. Les documents sont écrits en anglais, langue principale de publication des articles scientifiques et des données en astrophysique.

Activités :
– Extraction, standardisation, description et mise en forme des données à l’aide d’outils spécifiques.
– Vérifications des données avant ingestion dans la base de données VizieR.
– Interaction avec les astronomes du Centre de Données astronomiques de Strasbourg (CDS).

———————————————————————————————-
(english version)
As part of a team of documentalists, astronomers and computer scientists, you will be responsible for describing, indexing, standardising and checking tabular data from the main astrophysical journals to feed the VizieR database, with the aim of optimising their scientific exploitation. The documents are written in English, the main language of publication of scientific articles and data in astrophysics.

Activities
– Extraction, standardisation, description and formatting of data using specific tools.
– Verification of data before ingestion into the VizieR database.
– Interaction with the astronomers of the Strasbourg Astronomical Data Centre (CDS).

Profil du candidat :
Bac + 3 ans minimum

Formation et compétences requises :
Connaissances :
– Expérience informatique souhaitée (UNIX, script, python, …)
– Astronomie ou capacité à apprendre l’astronomie (être à l’aise avec les données scientifiques et numériques)
– Anglais lu indispensable

Compétences opérationnelles :
– Rigoureux et autonome
Compétences comportementales :
– Goût du travail en équipe souhaité

——————————————————————————————————-
(english version)
Knowledge :
– Computer experience welcomed (UNIX, python, scripts)
– Astronomy knowledge or ability to learn astronomy (comfortable with scientific and numerical data)
– English reading/comprehension essential
Operational skills:
– Rigorous and autonomous
– Taste for teamwork desired

Adresse d’emploi :
Centre de Données astronomique de Strasbourg (CDS)
Observatoire astronomique de Strasbourg (ObAs), UMR 7550
11, rue de l’Université 67000 Strasbourg

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Laboratoire Parole et Langage (UMR7309)
Durée : 24 mois
Contact : laurent.prevot@univ-amu.fr
Date limite de publication : 2022-02-14

Contexte :
The long-term goal of SUMM-RE is to improve algorithms for automatic meeting summarization and meeting minutes. The central hypothesis of the project is that such systems will benefit greatly from exploiting rich information carried by discourse relations (Explanations, Questions/Answers, Corrections…) and discourse structure (in the form of graphs). One of the major objectives of the project is therefore to develop an incremental discourse parser for spontaneous conversation, building on extant work by SUMM-RE members using weak supervision (Badene et al. 2019). Discourse parsing will be done on English (the AMI corpus) and French data, but the principal focus will be on a 100h corpus of meetings in French whose creation will be completed by the time the postdoc starts.

SUMM-RE: anr.fr/Projet-ANR-20-CE23-0017, https://labs.linagora.com/summ-re/

Sujet :
The postdoc recruited for this position will be in charge of (i) adapting models of discourse segmentation (e.g. Muller et al. 2019) to meeting-style conversation by building on recent advances with weak supervision (Gravellier et al. 2021) and integrating both speech and acoustic parameters in the segmentation model; (ii) applying insights from discourse segmentation, which provides the foundation for discourse parsing, to improve the incremental discourse parser; (iii) considering and developing mitigation strategies for working directly on ASR output (rather than on gold human transcribed data) for both discourse segmentation and parsing.

Profil du candidat :
Given these tasks we are looking for a candidates with as many of the following skills as possible:

– Experience with speech and ASR, and conversational speech in particular

– Dialogue/conversation/interaction analysis and modeling

– Machine Learning, in particular Weakly Supervised and Unsupervised approaches

– Multimodal (speech + text) Deep Representations for Natural Language Processing

– Multilingual model transfer

Formation et compétences requises :
Phd in Computational Linguistics / Natual Language Processing / Machine Learning.

A minimal command of French is desirable as the postdoc will be required to handle a large French corpus; mastery of French is, however, not required.

Adresse d’emploi :
The postdoc will ideally be hosted by the Laboratoire Parole et Langage (LPL), though exceptions will be considered for candidates who wish to be based at IRIT.

A curriculum vitae and a list of publications should be sent to Laurent Prévot (laurent.prevot@univ-amu.fr) no later than February 18th, but we strongly encourage potential candidates to submit their applications as soon as possible, as we might fill the position earlier.

Laboratoire Parole et Langage: https://www.lpl-aix.fr/en/welcome-to-lpl/

LINAGORA Labs: labs.linagora.com

MELODI @ IRIT : https://www.irit.fr/departement/intelligence-artificielle/equipe-melodi/

Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : Laboratoire ETIS – Cergy
Durée : 6 mois
Contact : hajer.baazaoui@ensea.fr
Date limite de publication : 2022-02-15

Contexte :
La traçabilité d’une manière générale et celle des médicaments constitue un domaine d’actualité compte tenu de la complexité des problèmes et exigences économiques, du vieillissement de la population, … Si ces problèmes viennent à être résolus de manière transversale et adéquate, ils peuvent conduire à des améliorations significatives pour toutes les parties prenantes, y compris les organisations de santé, les utilisateurs finaux et l’industrie pharmaceutique. L’objectif global du projet dans le cadre duquel s’inscrit le présent poste, est d’étudier et de définir un nouveau modèle de traçabilité pharmaceutique, qui améliorerait la protection des utilisateurs. Cette problématique nécessite des compétences complémentaires dans les domaines de la sémantique, de la gestion des connaissances, alliés à des approches d’optimisation multi-objectif et des technologies d’actualité notamment la blockchain,… Les premiers travaux entamés dans le cadre de ce projet, ont permis d’étudier les modèles existants et les facteurs permettant la traçabilité. Ensuite, de définir un modèle de traçabilité pharmaceutique, qui améliorerait la protection de la contrefaçon. D’autre part, un modèle pour l’intégration des données basée sur les ontologies, a été proposé. L’approche sémantique d’intégration virtuelle des données permet de fournir une vue intégrée et liée des données. Un prototype a été implanté et expérimenté.

Sujet :
La personne recrutée se verra confier les travaux déjà réalisés dans le cadre du projet décrit plus haut, ainsi que les prototypes développés. Les principales missions seront de:
-maîtriser les travaux existants et de valider le modèle de mapping des données proposées pour l’intégration des données
-intégrer les parties développées au framework à base de blockchain pour la traçabilité des médicaments
-participer à la rédaction de l’extension du présent projet

Le/la candidat(e) retenu(e) participera aux:
– réunions qui seront organisées
– interactions avec les partenaires du projet
– différentes tâches de rédaction des livrables du projet (rapport, publications scientifiques, nouvelle soumission).

Profil du candidat :
La personne recrutée doit être titulaire d’un doctorat en informatique (spécialités: Intelligence Artificielle, Blockchain, Systèmes d’Information, Bio-informatique,…).
Une forte capacité à comprendre les besoins, argumenter les choix et des compétences en développement sont indispensables.
La maîtrise des langages java et python est nécessaire.
Une expérience en rapport avec les données de santé/médicales est souhaitée.
Une forte capacité à rédiger en anglais et en français

Formation et compétences requises :
Doctorat en informatique

Adresse d’emploi :
Laboratoire ETIS – équipe MIDI – CY Cergy Paris Université

Document attaché : 202201271655_fiche poste IR ou postdoc info VF.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : ENSAI
Durée : CDI ou CDD 3 ans
Contact : romaric.gaudel@ensai.fr
Date limite de publication : 2022-02-15

Contexte :

ENSAI, the French graduate-level engineering school specialized in Statistics, Data Science and Economics, is currently inviting applications for a position as Associate or Assistant Professor in Computer Science and Machine Learning. The appointment starts in September, 2022, at the earliest. At the level of Assistant Professor, the position is for an initial three-year term renewable for another three years before the tenure evaluation. At the level of Associate Professor, the position is tenured.

Sujet :
Salary is competitive according to qualifications. The teaching duties are reduced compared to French university standards. At the appointment, knowledge of French is not required but it is expected that the appointee will acquire a workable knowledge of French in a reasonable time. The school offers resources to learn French.

Profil du candidat :
PhD in Computer Science with an expertise in Machine Learning. ENSAI is involved in the EUR Digisport and the EUR CyberSchool, so knowledge on related fields would be a plus.

Applicants will have demonstrated strong ability to teach courses and supervise projects in Computer Science, up to Master level, to students with a major in Statistics.

At the Associate Professor level, the candidate will have an outstanding research record and is expected to supervise PhD students.

Formation et compétences requises :
cf. Profil

Adresse d’emploi :
Campus de Ker Lann
rue Blaise Pascal BP37203
35172 BRUZ CEDEX

Document attaché : 202201201722_Position_ML.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : ImViA
Durée : 12 – 24 months
Contact : yannick.benezeth@u-bourgogne.fr
Date limite de publication : 2022-02-17

Contexte :
The ImViA lab at University Burgundy (Dijon – France), together with Honda Research Institute (Japan), invites applications for postdoctoral research positions in deep learning for video analysis.

Interested candidates should submit their CV, letter(s) of reference, and a brief research statement describing their background and research interests and how they align with the project emailed to Yannick Benezeth (yannick.benezeth@u-bourgogne.fr). The call will remain open until the position is filled. The postdoc contract will start as soon as possible.

Sujet :
The postdoctoral researcher will work on improving existing deep learning models to estimate physiological signals from the video signal. Remote photoplethysmography (rPPG) is a recent technique for estimating heart rate and other vital signs by analyzing subtle skin color variations using regular cameras (see [1] for an interesting review). More recently, end-to-end approaches based on deep learning have also been used. We will seek to extend existing work by improving current models, focusing on night vision applications. The candidate will take part in ongoing projects and possibly initiate new research within the team.

[1] Rouast, P. V., Adam, M. T., Chiong, R., Cornforth, D., & Lux, E. (2018). Remote heart rate measurement using low-cost RGB face video: a technical literature review. Frontiers of Computer Science, 12(5), 858-872.

Profil du candidat :
The postdoctoral researcher will work in Dijon – France in collaboration with researchers from the Honda Research Institute in Japan. This fellowship has a duration of 12 months with possibility of extension. As part of this postdoc, we can offer generous support for professional travel and research needs.

We are seeking a highly qualified and motivated candidate with a Ph.D. in Computer Vision, Machine Learning, Image processing, Biomedical Engineering, or a closely related field with a relevant scientific track record on significant computer vision conferences/journals as well as experience on deep learning techniques and frameworks.

Formation et compétences requises :
Ph.D. in Computer Vision, Machine Learning, Image processing, Biomedical Engineering

Adresse d’emploi :
The postdoctoral researcher will work in Dijon – France.

Offre en lien avec l’Action/le Réseau : SimpleText/Doctorants

Laboratoire/Entreprise : GIPSA-LAB Grenoble / Woods Hole Oceanographic In
Durée : 18 months
Contact : jerome.mars@gipsa-lab.fr
Date limite de publication : 2022-02-18

Contexte :
Machine learning (ML), and more recently deep learning, are the most recent revolutions in computer science. However, the impact of ML on underwater acoustics stays limited. This is largely due to two factors inherent to the acoustic inversion problem: large datasets with reliable annotations are usually not available, and the signal degradation due to propagation and noise is more severe than for other classical ML applications.

Sujet :
In this project, the postdoctoral investigator will embed ML methods into the traditional underwater acoustics inverse problem (source localization and environmental inversion). Of particular interest will be to replace the non-linear forward model (i.e. acoustic propagation code) with an approximated version obtained using a neural network. Such methods, proposed by the geoscience community [1], are known to accelerate the resolution of the inverse problems [2], but have never been explored in ocean acoustics. Independent research is expected, and other ideas by the postdoctoral investigator will be encouraged.

Any progress made by the postdoctoral investigator will be directly integrated into current work within our group on related topics [3,4], which provide a basis for broad-impact contributions and long-term international collaboration

References:
[1] Krasnopolsky, V. M., & Schiller, H. (2003). Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements. Neural Networks, 16(3-4), 321-334.
[2] Hansen, T. M., & Cordua, K. S. (2017). Efficient Monte Carlo sampling of inverse problems using a neural network-based forward—Applied to GPR crosshole traveltime inversion. Geophysical Journal International, 211(3), 1524-1533.
[3] Bonnel, J., Dosso, S. E., Knobles, D. P., & Wilson, P. S. (2021). Transdimensional Inversion on the New England Mud Patch Using High-Order Modes. IEEE Journal of Oceanic Engineering.
[4] Baron V., Bouley S., Muschinowski M., Mars J., Nicolas. B, (2019), Drone localization and identification using an acoustic array and supervised learg ning (2019), Artificial Intelligence and Machine learning in defense application Conf.

Profil du candidat :
Requirements: Applicants must have a PhD in a field relevant to the project.

Applications from candidates with a Ph.D in machine learning / data science and a strong interest in acoustics or ocean science, as well as applications from candidates with a Ph.D in acoustics and a proven background in machine learning are welcome.

Before hiring, the applicant file must be validated by the French Department of Defense.

Preference will be given to applicants from the European Union.

Formation et compétences requises :
Applications from candidates with a Ph.D in machine learning / data science and a strong interest in acoustics or ocean science, as well as applications from candidates with a Ph.D in acoustics and a proven background in machine learning are welcome.

Specifications: the position is fully funded for 18 months, but could be renewed upon scientific outcome and performance.

The monthly salary will range from 2,600 € to 3,800 € based on experience.

Adresse d’emploi :
Application: An online application form must be filled:
https://bit.ly/3FvefVL.

The applicant must a CV and a cover letter, and provide the contacts of at least two references. Other material (e.g. research statement, relevant publications, etc.) can also be included. Review of applications will begin immediately and continue until the position is filled.

Document attaché : 202201181620_postdoc_gipsa_OA_AI_JIMdocx.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LOP
Durée : CDI
Contact : jean.marc.delouis@ifremer.fr
Date limite de publication : 2022-02-18

Contexte :
Bonjour à tous

Un poste IRD d’Ingénieur d’étude en intelligence artificielle appliqué à l’océanographie en mobilité interne et externe à l’IRD pour le LOPS. Deadline 18 février

Poste possiblement ouvert dans les mois qui viennent sur concours externe si non pourvu par la mobilité

https://www.ird.fr/ingenieur-en-intelligence-artificiellemachine-learning-applique-loceanographie

Merci beaucoup de diffuser largement dans vos réseaux et UMR

(Désolée pour les non concernés)

Sujet :
https://www.ird.fr/ingenieur-en-intelligence-artificiellemachine-learning-applique-loceanographie

Profil du candidat :
https://www.ird.fr/ingenieur-en-intelligence-artificiellemachine-learning-applique-loceanographie

Formation et compétences requises :
https://www.ird.fr/ingenieur-en-intelligence-artificiellemachine-learning-applique-loceanographie

Adresse d’emploi :
https://www.ird.fr/ingenieur-en-intelligence-artificiellemachine-learning-applique-loceanographie

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LS2N, Nantes, France with mobility at NII Tokyo, J
Durée : 12 mois
Contact : Fabrice.Guillet@univ-nantes.fr
Date limite de publication : 2022-02-28

Contexte :
Title : Combining graph embedding and topic modelling for ontology/KG learning from large scale data

Key words : Topic modelling, Knowledge graph learning, Ontology learning, Graph embedding, Deep learning

Description. Ontology learning from the web data is a major challenging topic within the semantic web field and many approaches have been developed to tackle it. However, due to sparsity and heterogeneity of data, they lack to provide good quality results with a high semantical relevance for humans. The post-doc work aims to define a new approach for ontology learning/knowledge graph learning by incorporating embedded knowledge graphs in a clustering technique (topic modelling) dealing better with the sparsity and the heterogeneity of texts available in the Web and the semantical relevance of the results. The research domain of this post-doc position is model learning, linked data and graph embedding for ontology/knowledge graph learning from texts. Model learning/Topic modelling is one of the area expertise of the DUKe team (Data User Knowledge) of LS2N, one of the France’s leading public research labs in digital sciences. Linked data, graph building from texts and knowledge graph embedding are fields of expertise of the Japanese Ichise Laboratory from the National Institute of Informatics (NII), one of the leading research institute in Japan.

Duration : 12 months from (1 January 2022 -31 December 2022) including a mobility of 3 months in Japan .

Localization : Polytech Nantes, France , Ichise Laboratory, Tokyo Japan

Salary: 2900€ gross monthly + mobility expenses in Japan, during three months, about 350.000 yens / month.

Application: Candidates should have a PhD in computer science or applied mathematics, with strong experience in machine learning and related coding ecosystems in python. A background in semantic web and probability/statistics would be a plus.
Applicants should send a full CV including a complete list of publications and completed projects, a cover letter, and letters of recommendation or the names of two people who have worked with them.

Contact: Mounira Harzallah (Mounira.harzallah@univ-nantes.fr, Fabrice Guillet fabrice.guillet@univ-nantes.fr), DUKe, LS2N, France

Sujet :
Description. Ontology learning from the web data is a major challenging topic within the semantic web field and many approaches have been developed to tackle it. However, due to sparsity and heterogeneity of data, they lack to provide good quality results with a high semantical relevance for humans. The post-doc work aims to define a new approach for ontology learning/knowledge graph learning by incorporating embedded knowledge graphs in a clustering technique (topic modelling) dealing better with the sparsity and the heterogeneity of texts available in the Web and the semantical relevance of the results. The research domain of this post-doc position is model learning, linked data and graph embedding for ontology/knowledge graph learning from texts. Model learning/Topic modelling is one of the area expertise of the DUKe team (Data User Knowledge) of LS2N, one of the France’s leading public research labs in digital sciences. Linked data, graph building from texts and knowledge graph embedding are fields of expertise of the Japanese Ichise Laboratory from the National Institute of Informatics (NII), one of the leading research institute in Japan.

Duration : 12 months from (1 January 2022 -31 December 2022) including a mobility of 3 months in Japan .

Localization : Polytech Nantes, France , Ichise Laboratory, Tokyo Japan

Salary: 2900€ gross monthly + mobility expenses in Japan, during three months, about 350.000 yens / month.

Profil du candidat :
Application: Candidates should have a PhD in computer science or applied mathematics, with strong experience in machine learning and related coding ecosystems in python. A background in semantic web and probability/statistics would be a plus.
Applicants should send a full CV including a complete list of publications and completed projects, a cover letter, and letters of recommendation or the names of two people who have worked with them.

Contact: Mounira Harzallah (Mounira.harzallah@univ-nantes.fr, Fabrice Guillet fabrice.guillet@univ-nantes.fr), DUKe, LS2N, France

Formation et compétences requises :
see above

Adresse d’emploi :
see above

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : BII, A*STAR, Singapore
Durée : +12 mois
Contact : eddy_tan@bii.a-star.edu.sg
Date limite de publication : 2022-02-28

Contexte :
We are looking to hire a postdoc to work on an exciting interdisciplinary collaboration project between the Bioinformatics Institute (BII, A*STAR) and the Institute of Bioengineering and Bioimaging (IBB, A*STAR). In this two years project, we are aiming to develop a deep learning model for the prediction of potential antimicrobial agents using a combination of well-established public datasets and an in-house database. Prior knowledge in deep learning is NOT REQUIRED, although candidates with deep learning expertise are welcomed. Training on deep learning is provided on the job.

Sujet :
Artificial intelligence (AI) techniques have been employed in the niche field of macromolecular therapeutics to accelerate the development of new, highly selective antimicrobial polymers. However, current AI methods utilize simplistic representations in creating models to identify non-hemolytic antimicrobial polymers. As such, we at the Bioinformatics Institute (BII, A*STAR) are looking to hire a postdoc to work on an exciting interdisciplinary collaboration project with the Institute of Bioengineering and Bioimaging (IBB, A*STAR). In this two years project, we are aiming to develop a deep learning model for the prediction of potential antimicrobial agents using a combination of well-established public datasets and an in-house database. You will be working among a team consisting of people with different domain expertise such as biologists, polymer chemists, bioinformaticians, and AI scientists. Therefore, the ideal candidate should be a team player who can formulate and execute creative ideas for a given problem. Join us if you are excited about improving the quality of healthcare and interested in learning AI skills.

Responsibilities:
1. Assist in the development and maintenance of an antimicrobial database
2. Develop and implementation of novel AI algorithms for the prediction of antimicrobial and hemolytic activity
3. Periodically present the progress to the group and submit the research findings to top-tier journals and conferences

For more information, please visit the CVPD websites: http://web.bii.a-star.edu.sg/~leehk/ and
https://www.a-star.edu.sg/bii/research/ciid/cvpd

Interested applicants are welcome to email a full CV and a one-page letter of intent summarizing past experience and strengths as well as contact details of two referees to Eddy (eddy_tan@bii.a-star.edu.sg), or Malay Singh (malay_singh@bii.a-star.edu.sg).

Profil du candidat :
You will be working in a team of AI researchers who have a deep understanding of the fundamentals of deep learning and have considerable experience in applying deep learning to different problems. You will have the opportunity to learn and hone your AI skills through this project as well as by learning from other on-going projects in the team. You will be trained to be in the very niche area of applying graphical neural networks for macromolecular therapeutic applications. This is the differentiating factor for you as a postdoctoral research fellow at our lab. You will also learn to sharpen your communication, collaboration, project management, and leadership skills.

Formation et compétences requises :
Basic Requirements:
1. Ph.D. in Computer Engineering, Computer Science, Mathematics, Statistics, or related discipline.
2. Proficient in python programming
3. Excellent communication (verbal and written) and presentation skills
4. Curious, detail-oriented, and analytical, with a proven ability to learn quickly
5. Ability to work as a team player, which includes the willingness to contribute ideas and knowledge with peers
6. Ability to adjust according to the pace of the project and its changing requirements.

Following skill sets are optional but will be advantageous for applicants to highlight any relevant experience(s) in the submitted CV.
1. Experience in machine/deep learning with a focus on graphical neural networks
2. Knowledge in chemistry, biochemistry, and antimicrobial agents
Having worked in multidisciplinary teams

Adresse d’emploi :
BII, A*Star, Singapore

Document attaché : 202201040817_JD_ET.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : BII, A*STAR, Singapore
Durée : + 12 mois
Contact : malay_singh@bii.a-star.edu.sg
Date limite de publication : 2022-02-28

Contexte :
We are looking to hire a postdoc to work on an interdisciplinary project involving the development of AI models to quantify genetic intra-tumor heterogeneity in tissue whole slide images. This project is between the Bioinformatics Institute (BII, A*STAR), Genome Institute of Singapore (GIS, A*STAR), National Cancer Centre of Singapore (NCCS) and Singapore General Hospital (SGH). This project is supported by datasets derived from more than 5000 patients and strong clinical domain knowledge. Prior experience in deep learning is not required although candidates with deep learning experience are welcome. Training on deep learning is provided on the job. Please send your interest and CV to Malay Singh (malay_singh@bii.a-star.edu.sg) and Mahsa Paknezhad (mahsap@bii.a-star.edu.sg).

Sujet :
Position:
Postdoctoral position for developing an AI model to learn and quantify genetic intra-tumor heterogeneity in the tissue whole slide images.

Project Description: We are looking for a postdoctoral researcher to work with us on a collaboration project between the Bioinformatics Institute (BII, A*STAR), Genome Institute of Singapore (GIS, A*STAR), National Cancer Centre of Singapore (NCCS), and Singapore General Hospital (SGH). This project is supported by datasets derived from more than 5000 patients and strong clinical domain knowledge. We aim to design an AI model to quantify intra-tumor heterogeneity in histopathological tissue whole slide images. Cancer cells evolve into genetically diverse clonal populations in response to immune suppression via random mutation. High genetic diversity within the tumor cells leads to poor patient prognosis and is known as genetic intra-tumor heterogeneity (ITH). ITH is usually quantified via genomic methods. In this project, we will develop an AI model to quantify ITH using the imaging modality. We are looking for a creative mind with strong communication skills and a team player who can lead the project from forming ideas to methods development leading to publishing results in top-tier journals.

Profil du candidat :
What is in it for you?
You will be working in a team of AI researchers who have a deep understanding of the fundamentals of deep learning and have considerable experience in applying deep learning to different problems. Our group has a well established reputation in developing AI solutions for digital pathology. You will have the opportunity to learn and hone your AI skills through this project as well as by learning from other on-going projects in the team. You will be trained to be in the very niche area of applying deep learning for digital pathology applications. You will learn both AI and digital pathology knowledge in this project. This will be the differentiating factor for you as an AI scientist at our lab. You will also learn to sharpen your communication, collaboration, project management and leadership skills.

Responsibilities
1. Develop AI methodologies to perform computer assisted diagnostics for digital pathology.
2. Work closely with clinicians to fully understand the digital pathology datasets including histology slides as well as genomic data.
3. Design and develop the AI model to quantify the ITH in the histopathological images.
4. Design and conduct the experiments to assess the proposed AI models’ performance.
5. Collaborate with the team members via discussions, study groups, guiding students/ interns/research officers.
Periodically present the progress to the group and submit the research findings to top-tier journals and conferences

Formation et compétences requises :
1. Ph.D with a strong background in Computer Science/Mathematics/Statistics/Biomedical Engineering or relevant fields.
2. Prior knowledge in machine learning and prior domain knowledge in digital pathology is NOT REQUIRED. Training for these domains will be provided on the job.
3. Experience and versatility in programming especially in python.
4. Familiarity with PyTorch and/or TensorFlow is NOT REQUIRED but would be a plus.
5. Good communication skills, a team player and willing to share ideas and knowledge with peers.
6. Candidates should be able to work in a fast paced environment.

For more information, please visit the CVPD websites: http://web.bii.a-star.edu.sg/~leehk/ and
https://www.a-star.edu.sg/bii/research/ciid/cvpd

Adresse d’emploi :
BII, A*STAR, Singapore

Document attaché : 202201040827_JD_MS.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : BII, A*Star, Singapore
Durée : +12 mois
Contact : mahsap@bii.a-star.edu.sg
Date limite de publication : 2022-02-28

Contexte :
We are looking for a postdoctoral researcher to work with us on an exciting collaboration between the Bioinformatics Institute (BII), the Institute for Infocomm Research (I2R) and Singapore General Hospital. The objective is to design an AI model that can help hospitals by learning from our dataset which contains a large set of cancer types to perform a diverse set of diagnostic tasks such as cancer grading and gland segmentation. Prior experience in deep learning is not required although candidates with deep learning experience are welcome. Join us if you are excited about improving the quality of healthcare and interested in learning AI skills. Please send your interest and CV to Mahsa Paknezhad (mahsap@bii.a-star.edu.sg) and Eddy Tan (eddy_tan@bii.a-star.edu.sg).

Sujet :
Position:
Postdoctoral Research Fellow position for developing an AI model to learn heterogeneous tasks in parallel. An application to digital pathology.

Project Description:
Training deep learning (DL) models on multiple heterogeneous tasks is one of the main steps in the direction of offering robust and generalizable AI solutions. We are seeking a postdoctoral research fellow to participate in an exciting collaboration between the Bioinformatics Institute (BII), the Institute for Infocomm Research (I2R) and Singapore General hospital. The aim is to design an AI model that can help hospitals by learning a diverse set of diagnostic tasks such as breast cancer grading and prostate gland segmentation as tasks are introduced through time. An AI model that is sufficiently smart to optimise the required network resources and to reduce the necessary dataset size to deliver a desirable performance for each task. To achieve these objectives the proposed AI model should be novel in many aspects. For instance, it should automatically decide which part of the network to assign to a new task, it should automatically expand itself if necessary to learn a new task, it should find similarities between tasks and share resources between similar tasks and it should avoid sharing resources between dissimilar tasks. We look for a creative mind with strong communication skills. A team player who can lead the project from forming ideas, to development, analysis and publishing results in top-tier journals.

Profil du candidat :
What is in it for you?
You will be working in a team of AI researchers who have a deep understanding of the fundamentals of deep learning and have considerable experience in applying deep learning to different problems. You will have the opportunity to learn and hone your AI skills through this project as well as by learning from other on-going projects in the team. You will acquire a deep knowledge of the cutting edge techniques in continual learning, reinforcement learning, and image processing. You will also learn to sharpen your communication, collaboration, project management and leadership skills.

Responsibilities:
(1) Maintain multiple histopathology datasets belonging to different healthcare problems
(2) Work closely with clinicians to fully understand the healthcare problems and the histopathology datasets
(3) Develop a novel AI algorithm that can expand (if necessary) and train parts of a neural network on different healthcare problems in parallel while providing efficiency in terms of task performance and network resources
(4) Carefully design experiments for assessment of the proposed AI algorithm
(5) Collaborate with peers, supervise interns and research officers
Periodically present the progress to the group and submit the research findings to top-tier journals and conferences

Formation et compétences requises :
Requirements:
(1) PhD in areas such as Computer Science, Machine Learning, Deep Learning, Computer Vision, Mathematics, Probabilities
(2) Sufficient experience in programming in python
(3) Familiarity with PyTorch or Tensorflow libraries is NOT REQUIRED but would be a plus
(4) Prior knowledge in deep learning is NOT REQUIRED but would be a plus
(5) Familiarity with Reinforcement Learning is NOT REQUIRED but would be a plus
(6) Good verbal and written communication and troubleshooting skills
(7) Curious, detail oriented, and analytical, with a proven ability to learn quickly
(8) A team player who is willing to share ideas and knowledge with peers
For more information, please visit the CVPD websites: http://web.bii.a-star.edu.sg/~leehk/ and https:// www.a-star.edu.sg/bii/research/ciid/cvpd

Adresse d’emploi :
BII, A*Star, Singapore

Document attaché : 202201040823_JD_MP.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : MAP5 (UMR CNRS 8145, Université de Paris)
Durée : CDI
Contact : antoine.chambaz@u-paris.fr
Date limite de publication : 2022-03-01

Contexte :
Ancrée au coeur de la capitale, Université de Paris figure parmi les établissements français et internationaux les plus prestigieux grâce à sa recherche de très haut niveau, ses formations supérieures d’excellence, son soutien à l’innovation et sa participation active à la construction de l’espace européen de la recherche et de la formation. Labellisée Idex depuis mars 2018, Université de Paris s’appuie sur ses enseignants, ses chercheurs, ses enseignants-chercheurs, ses personnels administratifs et techniques, ses étudiants, pour développer des projets scientifiques à forte valeur ajoutée, et former les hommes et les femmes dont le monde de demain a besoin. Des sciences exactes et expérimentales aux sciences humaines et sociales, en passant par la santé, Université de Paris a fait de l’interdisciplinarité un marqueur fort de son identité. Elle compte aujourd’hui 64 000 étudiants, 7 500 personnels, 138 laboratoires, répartis au sein de ses trois grandes Facultés en Santé, Sciences et Société et Humanités et de l’institut de physique du globe de Paris. Rejoindre Université de Paris c’est faire le choix de l’exigence et de l’engagement au service de valeurs fortes ; celles du service public, de la rigueur scientifique et intellectuelle mais aussi de la curiosité et de l’ouverture aux autres et au monde.

Sujet :
Statistique et applications.

L’enseignement se fera au sein de l’UFR Mathématiques et Informatique. La personne recrutée devra être à même d’enseigner les Mathématiques et plus spécifiquement les Statistiques en Licence et Master, notamment l’Analyse de données en grande dimension, l’Apprentissage ou les Statistiques Mathématiques. Elle devra s’investir dans la responsabilité et la gestion du Master Ingénierie Mathématique et Biostatistique (IMB, niveaux Master 1 et 2) et du Master de Mathématiques Appliquées « Mathématiques Modélisation Apprentissage » (MMA, niveaux Master 1 et 2). Enfin, la personne recrutée sera amenée à s’impliquer dans les instances de l’UFR (Conseil d’UFR, Conseil Scientifique Local), devra endosser des responsabilités au niveau du Laboratoire ou des instances de la Faculté des Sciences de l’Université.

Postes d’enseignants-chercheurs mis au concours en 2022

Profil du candidat :
Le recrutement proposé est destiné à remplacer le départ d’un PR de l’équipe de statistique, qui est à ce jour composée de 12 membres permanents (4 en poste à l’UFR Mathématiques et Informatique et 8 à l’IUT de Paris — Rives de Seine) et de 5 membres émérites. Les candidatures pourront permettre soit de renforcer l’un des nombreux thèmes de recherche en statistique développés au sein de l’équipe (voir le site du Laboratoire), soit d’apporter une expertise nouvelle ou peu représentée. Notamment, une expertise dans le domaine des données massives et de grande dimension ainsi que des applications aux sciences du vivant seront appréciées. La personne recrutée apportera également un soutien à l’encadrement de projets de Master, ainsi qu’à l’encadrement doctoral et postdoctoral, et s’impliquera dans des réponses aux nombreux Appels à Projets.

Formation et compétences requises :
Habilitation à diriger des recherches, ou équivalence

Adresse d’emploi :
MAP5 (UMR CNRS 8145, Université de Paris)
Campus Saint-Germain des Prés
45 rue des Saints-Pères
75270 Paris cedex 06

Document attaché : 202111221706_FichePoste_PR26_MAP5.pdf