Verimag

A Post-Doc Position at the Tempo group

The Timed and Hybrid group (Tempo) at VERIMAG, one of the world-wide leading groups in cyber-physical systems, is looking for candidates for a 1-year Post-doc position starting in January 2018. The salary is between 2200-2500 Euros per month, depending on the candidate qualifiactions, medical insurance included. There might be a potential for prolongation from other sources

The candidate will work in the local project Cyber-Physical Data Mining for Predictive Maintenance supervised jointly by Oded Maler from Verimag and Eric Gaussier from LIG. The project combines ideas from verification and runtime verification (monitoring temporal logics and regular expressions for real-valued signals) to classification, pattern matching, learning and data mining for dynamic behaviors.

Motivated candidates with a PhD degree and a solid background in a non-empty subset of computer science, formal methods, machine learning, signal processing or statistical reasoning, are kindly asked to send (e-mail with "Post-Doc-candidate" in the title) a CV and motivation letter to Oded.Maler imag.fr

Candidates are strongly advised to read the project description before applying to test their qualification

The Grenoble area, in addition to the surrounding skiable mountains, features one of Europe’s largest concentrations of academic/industrial research and development with a lot of students and a relatively-cosmopolite atmosphere. You can easily reach Lyon (1 hour), Geneva (1.5 hours), Torino (2 hours), Paris (3 hours by train) and Barcelona (6 hours).

VERIMAG, http://www-verimag.imag.fr is a leading academic lab in verification and model-based design of embedded systems. Its past contributions include model checking (J. Sifakis, Turing Award 2007), the data-flow language Lustre underlying the SCADE programming environment for safety-critical systems, as well as pioneering contributions to the study of timed and hybrid systems and its applications.

The AMA team (dAta analysis, Modeling and mAchine learning) was created in LIG in January 2011 to work on machine learning and information modeling for complex data. Within this framework, the team is interested in developing new theoretical tools, as well as new prototypes to be deployed in classification or simulation settings, for example. The research activity in the AMA team is structured in the following three main themes: data analysis and learning theories, learning and perception systems, modeling social networks.


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