and Machine Learning at INRA Toulouse, France*
The interdisciplinary institute in artificial intelligence of Toulouse,
named the Artificial and Natural Intelligence Toulouse Institute
(ANITI), is one of four institutes spearheading research on AI in
France. Part of a 24 chairs' program funded by ANITI, Thomas Schiex's
chair is on /Pushing the computational frontiers of reasoning with
logic, probabilities and preferences/. The chair and co-chair Simon de
Givry, working in a bioinformatic team at INRA Toulouse, are seeking a
postdoctoral fellow. The position is available immediately. The project
will be funded on a contract for at most four years with net salary of
2600€ or more per month with some teaching (64 hours per year on average).
Constraint programming (CP) is an AI /Automated Reasoning/ technology
with tight connections with propositional logic. It offers a problem
modeling and solving framework where the set of solutions of a complex
(NP-hard) problem is described by discrete variables, connected by
constraints (simple Boolean functions). Together with propositional
satisfiability, it is one of the automated reasoning approaches of AI,
where problems are solved exactly to provide rigorous solutions to
hardware or software testing and verification, system configuration,
scheduling or planning problems.
Discrete Stochastic Graphical Models (GMs) define a /Machine Learning/
technology where a probability mass function is described by discrete
variables, connected by potentials (simple numerical functions). GMs can
be learned from data and the NP-hard problem of identifying a Maximum a
Posteriori (MAP) labelling is often solved /approximately/ to tackle
several problems in Image and Natural Language Processing, among others.
The Cost Function Network framework with its associated C++ open source
award-winning solver toulbar2 <https://github.com/toulbar2/toulbar2>,
developed in our team, combine the ideas of Constraint Programming and
Stochastic Graphical Models. By solving the so-called Weighted
Constraint Satisfaction problem, toulbar2 is capable of simultaneously
reasoning on logical information described as Boolean functions and
gradual, possibly Machine Learned, information described as local
numerical functions.
To process the available information, the solver relies on a guaranteed
hybrid branch and bound algorithm. In this algorithm, pruning follows
from a variety of mechanisms that can either simplify the problem at
hand, provide primal solutions (using local search, rounding or
incomplete tree-search), or provide dual solutions and lower bounds.
Parallel solving offers new opportunities to organize these various
mechanisms differently in time, to exploit problem decompositions, to
apply stronger primal/dual reasoning, and to use Machine Learning to
guide search or decide which mechanism to activate based on the current
solving and/or a collection of instances of the same problem.
Experiments will be performed on large collections of real problem
instances, many of which are not known to be currently solvable. This
includes the possible application of toulbar2 onto current exciting
problems in Computational Protein Design (CPD), in collaboration with
molecular modellers and biochemists, and in the context of the ongoing
development of a dedicated CPD software with applications in Health,
Bioenergy and Green Chemistry.
The position is specifically open to highly creative researchers that
may quickly want to develop and explore their own ideas. As such, we
expect that the PostDoc will be increasingly capable of injecting their
own ideas in the project, in interaction with all the members of the
project team as well as external collaborators, and contribute to the
supervision of PhD students.
*Candidate profile*
The PostDoc is at the intersection of CP, SAT, integer programming,
metaheuristics, and distributed computing. The ideal candidate should
therefore be familiar with CP or SAT algorithms. He or she may also
benefit from background knowledge in the weighted variants of SAT/CP, in
Integer Linear Programming, or in Stochastic Graphical Models
processing. Some experience in the design and implementation of
multi-threaded/distributed code is a nice plus. Good programming
abilities (in C++ ideally) will be required. Additional knowledge in
bioinformatics, biochemistry, and molecular modelling would be a plus in
the context of CPD applications.
*How to apply*
Please email your detailed CV, a motivation letter, and transcripts of
bachelor's degree and PhD in Computer Science to simon.de-givry@inra.fr
and thomas.schiex@inra.fr. Samples of published research by the
candidate and reference letters will be a plus.
APPLICATION DEADLINE FOR FULL CONSIDERATION: *December 1, 2019*.
https://mia.toulouse.inra.fr/images/2/26/PostDocANITI.pdf
https://en.univ-toulouse.fr/aniti
http://www7.inra.fr/mia/T/schiex/
http://www7.inra.fr/mia/T/degivry/
http://www7.inra.fr/mia/T/toulbar2/
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