Offres d’emploi

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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: renaud.sirdey@cea.fr
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 – https://liris.cnrs.fr/
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] univ-lyon1.fr

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,
etc.)

[1] Generating Personalized Recipes from Historical User Preferences,
Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, Julian McAuley,
https://arxiv.org, 2019
Date limite de candidature: 2020-01-20
Tél de contact: 04.72.43.19.97
Mail de contact: frederic.armetta@univ-lyon1.fr
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: https://www.telecom-paris.fr/en/home and https://datascienceandai.wp.imt.fr/
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] telecom-paris.fr
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] telecom-paris.fr, website : https://datascienceandai.wp.imt.fr/
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] telecom-paris.fr.
For all positions: 
Qualifications:
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
Candidature:
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.
Deadline:
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: florence.dalche@telecom-paris.fr
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, charlotte.laclau@univ-st-etienne.fr
Baptiste Jeudy, baptiste.jeudy@univ-st-etienne.fr
Date limite de candidature: 2019-11-15
Mail de contact: charlotte.laclau@univ-st-etienne.fr
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: l.robinault@foxstream.fr
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 : http://cazencott.info/dotclear/public/offers/2019-04-Postdoc-ANR.pdf

Date limite de candidature: 2019-07-31T
Mail de contact: chloe-agathe.azencott@mines-paristech.fr
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 (Thomas.walter@mines-paristech.fr), 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 (http://caor.mines-paristech.fr) 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 :
http://caor-mines-paristech.fr/fr/2019/03/chargee-de-recherche-en-intelligence-artificielle-pour-la-robotique-et-les-vehicules-autonomes/

Date limite de candidature: 2019-05-06
Mail de contact: Fabien.Moutarde@mines-paristech.fr
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 (http://www.litislab.fr/equipe/app/) and MIND (http://www.litislab.fr/equipe/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.

Profiles:
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: alexandre.pauchet@insa-rouen.fr