Connectionists: [MASH] Post-doc and phd positions at Idiap, CNRS, WIAS and INRIA

[Francois Fleuret]

Dear all,

We are starting a European project in machine learning in January 2010
and have several open positions.

Best regards,

-- 
Francois Fleuret                         http://www.idiap.ch/~fleuret/

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 * ABSTRACT

   The MASH project is a three-year research initiative which brings
   together five institutions with expertise in statistics, machine
   learning, goal planning and computer vision to investigate the
   collaborative design of complex hand-designed priors for machine
   learning.

   MASH is funded by the Information and Communication Technologies
   division of the European Commission, Cognitive Systems and Robotics
   unit, under the 7th Research Framework Programme.

   Research will start in January 2010 and will be carried out in
   Switzerland (IDIAP), France (CNRS and INRIA), Germany (WIAS) and
   Czech Republic (CVUT). Open positions are listed below.

   You can already register on http://www.mash-project.eu to get
   updated by mail on the status of the project.

 * SUMMARY

   The goal of the MASH project is to create new tools for the
   collaborative development of large families of feature extractors.
   It aims at starting a new generation of learning software with
   great prior model complexity.

   The project is structured around a web platform which will be open
   to external contributors early in 2010. It comprises collaborative
   tools such as a wiki-based documentation and a forum, and an
   experiment center which runs and analyzes experiments on a
   continuous basis.

   The applications targeted by the project are classical vision
   problems, and goal-planning in a 3D video game and with a real
   robotic arm.

   Contributors will participate to the project by uploading the
   source codes of "feature extractors" into the platform. Each one of
   these extractors processes an input image to generate values
   relevant to the system. This purposely broad definition spans from
   classical vision processing such as edge detector or color
   histogram estimation, to highly dedicated hand-designed templates
   or event-based memory for the robotic applications. The system
   concatenates all these extractors to create a very large feature
   vector, which is used as an input signal for a machine learning
   algorithm.

   In practice, anybody can upload such a module at any time. It will
   be immediately compiled and integrated in the next starting
   experiment. Preliminary performance measures will be provided in a
   matter of minutes, and complete results a few hours later. The
   system encourages contributors to improve upon the work on other
   and focus on the main weaknesses of the overall system.

   The scientific issues to be tackled along the course of the project
   are numerous, from standard machine learning questions such as
   learning and prediction with very large feature spaces and tight
   computational constraints, to original problems related to
   clustering in a functional space.

 * CONSORTIUM

   - Idiap Research Institute, Switzerland (IDIAP)

   - Centre National de la Recherche Scientifique, France (CNRS)

   - Weierstrass Institute for Applied Analysis and Stochastics,
     Germany (WIAS)

   - Institut National de Recherche en Informatique et en Automatique,
     France (INRIA)

   - Czech Technical University in Prague, Czech Republic (CVUT)

 * OPEN PHD POSITION AT IDIAP, SWITZERLAND

   Contact point: Dr. François Fleuret,
   francois.fleuret at idiap.ch,
   http://www.idiap.ch/~fleuret/

   On-line application at http://www.idiap.ch/~fleuret/hiring-mash.html

   The selected candidate will be a doctoral student at EPFL EDEE
   doctoral school. Research will be done at the Idiap Research
   Institute, under the supervision of Dr. François Fleuret.

   The research to be carried out will be the study of prediction
   techniques for goal-planning with very large feature space. The
   candidate will investigate prediction from images, mimicking to
   learn policies provided by human operators, and extensions of
   classical Markovian Modeling to the specificity of the MASH
   project.

   This work will mix theoretical developments in statistical learning
   with the implementation of algorithms working on real-world data.

   Applicants must have a strong background in mathematics and be
   self-sufficient in programming. They must be familiar with several
   of the following topics and interested in all of them:
   probabilities, applied statistics, information theory, signal
   processing, optimization, algorithmic, and C++ programming.

 * OPEN POSITIONS AT CNRS, FRANCE

   Contact point: Dr. Yves Grandvalet,
   yves.grandvalet at utc.fr,
   http://www.hds.utc.fr/~grandval/

   We have open PhD and PostDoc positions to develop clustering and
   block-clustering algorithms that will summarize heuristic behaviors
   across tasks. We aim at providing feedback to the heuristic
   designers by detecting similar heuristics across similar tasks,
   thus empowering designers to analyze coexisting strategies, and to
   detect critical failures.

   We will develop clustering and block-clustering methods based on
   probabilistic models and factorization techniques. We will also
   study the relationships between these approaches.

   The candidates will hold a Master/PhD in applied mathematics or
   computer science, and should have interest in both areas. They will
   work under the supervision of Y. Grandvalet and G. Govaert at the
   Heudiasyc lab. http://www2.hds.utc.fr/ at University of Technology
   of Compiègne http://www.utc.fr/the_university/index.php

 * OPEN POSITIONS AT WIAS, GERMANY

   Contact point: Dr. Gilles Blanchard,
   gilles.blanchard at wias-berlin.de,
   http://www.wias-berlin.de/people/blanchar/

   The research will be carried out at the Weierstrass Institute,
   Berlin, under the supervision of Dr. G. Blanchard; the selected
   candidate will be a doctoral student at the Humboldt University,
   Berlin.

   The research will concentrate on theoretical and practical
   developments of prediction techniques from a large set of
   heterogeneous features: aggregation, sparsification, grouping and
   reduction techniques, in particular under a strong limitation
   constraint of the computational burden. Automated construction of a
   similarity or distance measure between features will be also
   addressed.

   Specific Requirements: university degree (at least master/diploma)
   in mathematics, computer, science or engineering. We expect from
   potential candidates very good programming skills (C++) and at
   least basic knowledge in mathematical statistics, theory of machine
   learning and/or optimization.

 * OPEN POSITIONS AT INRIA, FRANCE

   Contact point: Dr. Olivier Teytaud,
   olivier.teytaud at inria.fr,
   http://www.lri.fr/~teytaud/

   The research will be carried out at the LRI, Université Paris-Sud,
   under the supervision of Olivier Teytaud (INRIA research
   fellow). We have open PhD and PostDoc positions.

   The research will focus on theoretical and practical developments
   of planing techniques from a large set of heterogenous features.

   Specific Requirements: university degree (at least master) in
   mathematics, computer science or engineering. We expect from
   potential candidates very good programming skills (C++) and at
   least basic knowledge in machine learning and/or planning.

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