[Job-offers-cs] Postdoctoral position in the ANITI 3IA Institute - INRA Toulouse, France

[Pekka Orponen]

-------- Forwarded Message --------
Subject: [DMANET] Postdoctoral position in the ANITI 3IA Institute - 
INRA Toulouse, France
Date: Fri, 22 Nov 2019 15:44:25 +0100
From: Thomas Schiex <thomas.schiex at inra.fr>
Organization: INRA
To: dmanet at zpr.uni-koeln.de

*Postdoctoral position in Artificial Intelligence, Discrete Optimization 
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 at inra.fr 
and thomas.schiex at 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/