[Liste-proml] Post Doctoral Position in Machine Learning at Univ. Lille

Olivier Pietquin olivier.pietquin at univ-lille1.fr
Mer 6 Avr 11:07:27 CEST 2016


We are happy to advertise for a *Post Doctoral Position in Machine 
Learning at Univ. Lille**
**
**Topic*
Machine Learning for Human-Robot Interaction

*Job description**
*This position is offered in the framework of the BabyRobot H2020 
(www.babyrobot.eu) project involving 8 partners across Europe (including 
France, Sweden, UK, Greece, Germany and Denmark). The project focuses on 
interactive robotics and especially on interaction with children.

Breakthroughs in core robotic technologies are needed to support this 
research mainly in the areas of motion planning and control in 
constrained spaces, gestural kinematics, sensorimotor learning and 
adaptation. In addition, new models of interaction need to be developed. 
Because of the human being in the loop, standard control theory can 
hardly be applied. For this reason, machine learning methods such as 
reinforcement and imitation learning have been identified as candidates 
to address these issues in a unied framework. Therefore, the applicant 
will be involved in research in core machine learning applied to control 
and interaction. Several directions of research are envisioned. First, 
recent works on stochastic games [3] applied to dialogue management [1] 
can be further investigated so as to be adapted to multimodal and 
multiparty interaction scenarios. Second, the "learning from 
demonstration" (LfD) [4] paradigm can be adapted to the adversarial case 
so as to transfer interactional behaviours from actual human-human 
interactions to machines. Other  topics can be investigated such as 
inverse reinforcement learning [2] or transfer learning.

*Profile*
The applicant should have completed a PhD in computer science, 
statistical learning or robotics. The ideal candidate will have a strong 
background in machine learning and especially in reinforcement learning 
or stochastic games. Experience in interactive systems (spoken dialogue 
systems, interactive robotics, human-machine interfaces) would be much 
appreciated. The recruited person will be involved in the management of 
the project, participate to consortium meetings and contribute to 
deliverables. Therefore, good communication skills and autonomy are 
mandatory. Preference will go to candidate with a strong publication record.

*Work environment**
*The position is offered in the Sequential Learning (SequeL) research 
team (joint team between Inria, Univ. Lille and CNRS) located in Lille, 
France. SequeL is a world-leading group in reinforcement learning, 
bandit theory and recommendation systems involving 30 members (including 
10 permanent staff members). The team's working language is English. 
The team is part of the French National Institute for Computer Science 
and Mathematics (Inria) as well as the Computer Science and Signal 
Processing laboratory of Lille (CRIStAL). Lille is the capital of the 
north of France, a metropolis with 1 million inhabitants, with excellent 
train connection to Brussels (30 min), Paris (1h) and London (1h30).

*How to apply*
The application should include a brief description of research interests 
and past experience, a CV, degrees and grades, motivation letter, 
relevant publications, letter(s) of recommendation and contact 
information to reference persons.

*Details**
*Application deadline: 30th of April 2016
Starting date: May or June 2016
Duration: 24 months (can be extended)
Salary (after taxes): 2100 euros

*Contacts**
*Olivier Pietquin: olivier.pietquin at univ-lille1.fr
Bilal Piot: bilal.piot at univ-lille1.fr

*References**
*[1] Merwan Barlier, Julien Perolat, Romain Laroche, and Olivier 
Pietquin. Human-machine dialogue as a stochastic game. In Proceedings of 
the 16th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL 
2015), pages 2{11, Prague (Czech Republic), September 2015.
[2] Edouard Klein, Matthieu Geist, Bilal PIOT, and Olivier Pietquin. 
Inverse Reinforcement Learning through Structured Classication. In 
Advances in Neural Information Processing Systems (NIPS 2012), pages 
1007{1015, Lake Tahoe (NV, USA), December 2012.
[3] Julien Perolat, Bruno Scherrer, Bilal Piot, and Olivier Pietquin. 
Approximate dynamic programming for two-player zero-sum markov games. In 
Proceedings of the International Conference on Machine Learning (ICML 
2015), Lille (France), July 2015.
[4] Bilal Piot, Matthieu Geist, and Olivier Pietquin. Learning from 
demonstrations: Is it worth estimating a reward function? In Hendrik 
Blockeel, Kristian Kersting, Siegfried Nijssen, and Filip Zelezny, 
editors, Proceedings of the European Conference on Machine Learning and 
Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 
2013), volume 8188 of Lecture Notes in Computer Science, pages 17{32, 
Prague (Czech Republic), September 2013. Springer.

-- 
Pr Olivier Pietquin
Univ. Lille - CRIStAL (UMR 9189) - SequeL team
Institut Universitaire de France

Parc Scientifique de la Haute-Borne
40 avenue Halley - Bât. A - Park Plaza
59650 Villeneuve d'Ascq, FRANCE

Tel : +33 (0) 3 59 57 79 09
Home : www.cristal.univ-lille.fr/~pietquin
e-mail : olivier.pietquin at univ-lille1.fr - olivier.pietquin at inria.fr

-- 
Pr Olivier Pietquin
Univ. Lille - CRIStAL (UMR 9189) - SequeL team
Institut Universitaire de France

Parc Scientifique de la Haute-Borne
40 avenue Halley - Bât. A - Park Plaza
59650 Villeneuve d'Ascq, FRANCE

Tel : +33 (0) 3 59 57 79 09
Home : www.cristal.univ-lille.fr/~pietquin
e-mail : olivier.pietquin at univ-lille1.fr - olivier.pietquin at inria.fr

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