Pour plus de détails : http://cazencott.info/dotclear/public/offers/2019-04-Postdoc-ANR.pdf

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).

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/

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.

Ils seront affectés à l’équipe de recherche Knowledge and Image Analysis for Decision making (KID) du Laboratoire de Génie Informatique et d’Ingénierie de Production (LGI2P).

Date limite de candidature : 30/04/2019

Contacts :
– M. Jacky Montmain, Directeur du LGI2P (Resp. équipe KID), jacky.montmain@mines-ales.fr, 04.34.24.62.94
– Mme Géraldine Brunel, Service de gestion des ressources humaines, geraldine.brunel@mines-ales.fr, 04.66.78.50.66

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

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