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Intitulé: Transfert de représentation pour la fouille de graphe
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, charlotte.laclau@univ-st-etienne.fr
Baptiste Jeudy, baptiste.jeudy@univ-st-etienne.fr

Application: The starting date is flexible, between February and September 2019.The candidate should indicate the preferred starting date in the application email.
Applications including a CV, a list of publications and an approximately two-page description of research interests should be sent by email to both supervisors’ emails. Applicants should also arrange at least one recommendation letter (to be sent to the same address). Informal inquiries can be sent to the same address

Date limite de candidature: 2019-09-15
Mail de contact: charlotte.laclau@univ-st-etienne.fr
Intitulé: Enseignant-chercheur en Intelligence Artificielle et Imagerie
Type d’offre d’emploi: Offre de poste dans l’académique
Détails de l’offre: Bonjour à tous,
Le département de Mathématiques et d’Informatique de l’ISAE-SUPAERO ouvre un poste d’Enseignant-chercheur en Intelligence Artificielle et Imagerie. La fiche de poste est disponible ici : https://www.isae-supaero.fr/IMG/pdf/fdp_576_-_ec_en_intelligence_artificelle_et_imagerie.pdfLes missions en enseignement portent essentiellement sur le cursus de Sciences des Données (niveau M2) et le/la titulaire sera amené(e) à prendre des responsabilités au sein de son périmètre d’expertise.
Le/la titulaire rejoindra le groupe « Systèmes Décisionnels » du département et son projet de recherche contribuera aux travaux en IA. Une implication au sein de SuReLI (Supaero Reinforcement Learning Initiative https://sureli.github.io/) sera particulièrement appréciée. 

Les candidats sont fortement encouragés à prendre contact avec les équipes du département afin d’affiner leur projet et de préciser les thématiques et les projets d’intérêt.
Date limite de réception des candidatures : 28 Février 2019

Pour complément d’information :
– Emmanuel Rachelson, emmanuel.rachelson@isae-supaero.fr
– Carlos Aguilar Melchor, carlos.aguilar-melchor@isae-supaero.fr

Cordialement,
Emmanuel

Date limite de candidature: 2019-03-29
Tél de contact: 0561338076
Mail de contact: emmanuel.rachelson@isae-supaero.fr
Intitulé: 14 PhD positions in Machine Learning Frontiers of Precision Medicine
Type d’offre d’emploi: Offre de thèse
Détails de l’offre: 14 PhD positions are open as part of the Initial Training Network on Machine Learning Frontiers in Precision Medicine, in places ranging from ETH Zürich (Switzerland) to IBM Haifa (Israel), University of Tartu (Estonia), Qlucore (Sweden) or Mines ParisTech (Paris).I myself will supervise a PhD student on learning from multi-modal data to improve cancer treatment, starting Fall 2019, at CBIO Mines ParisTech (Paris). 

To encourage mobility, the EU mandates that our recruits have not spent more than 12 months in the lab’s country over the 3 years prior to the recruitment date.

For details and application, go to https://euraxess.ec.europa.eu/jobs/363030 This first call closes on January 15, 2019.

Date limite de candidature: 2019-01-15T00:00:00+01:00
Mail de contact: chloe-agathe.azencott@mines-paristech.fr
Intitulé: Adaptive Computational Models of Human Movement to Support Learnability in Embodied Interaction
Type d’offre d’emploi: Offre de thèse
Détails de l’offre: Détail de l’offre : https://www.julesfrancoise.com/documents/ANR_ELEMENT_PhDProposal_LIMSI.pdf—————————————
ANR Project ELEMENT: ​Enabling Learnability in Embodied Movement Interaction
Supervision​: Yacine Bellik, Jules Françoise
Lab​: AMI Team, LIMSI-CNRS, Orsay (​https://www.limsi.fr/​)
Dates:​ Start Jan 2019 – Mar 2019 (duration: 3 years).
—————————————CONTEXTBecause memorizing and executing gestures is challenging for users, most current approaches to movement-based interaction consider intuitive interfaces and trivial gesture vocabularies. While these facilitate adoption, they also limit users’ potential for more complex, expressive and truly embodied interaction. Considering movement-based interaction beyond the mouse-keyboard paradigm, the ANR project ELEMENT (​Enabling Learnability in Embodied Movement Interaction​) proposes to shift the focus from ​intuitiveness​ towards ​learnability​: new interaction paradigms require users to develop specific sensorimotor skills compatible with – and transferable between, – digital interfaces (including video interface, mobile devices, internet of things, game interfaces). With learnable interactions, novice users should be able to approach a new system with a difficulty adapted to their expertise, then the system should be able to carefully adapt to the improving motor skills, and eventually enable complex, expressive and engaging interactions. The long-term aim is to foster innovation in multimodal interaction, from assistive technologies to media interaction in creative applications.The project ELEMENT is coordinated by Ircam (Paris), and also involves LRI (Orsay) and LIMSI (Orsay). The PhD candidate is expected to strongly interact with other PhD students and postdocs in the project, and will work in collaboration with all partners.
Date limite de candidature: 2018-12-02
Mail de contact: jules.francoise@limsi.fr