List of open Internships and PhDs

Most recent news

(list of all news)

Glucose is (finally) on Github

(17 May 2023)

Glucose is finally (also) distributed on Github. The idea is simply to allow official forks from Glucose versions. We used to distribute it only via the archives and the official...

Interview in the Regional Newspaper "Sud-Ouest" about Trustworthy AI

(03 May 2023)

A.I. has been the subject of many, many press articles in recent months. The exposure of tools such as chatGPT4 to the general public accelerates even more the need for...

Invitation to the Simons Institute (Berkeley)

(17 April 2023)

As part of the Simons Institute’s SAT Program (SAT Reunion Semester), I had the pleasure of being invited for a long stay in this famous research place about theoretical computer...

Lauch of the DIHNAMIC European Project

(20 March 2023)

The Dihnamic project has just been launched very officially. It is a large-scale European and regional project in which I act as a member of the advisory board. The project...

Dataquitaine 2023

(02 March 2023)

The sixth “Dataquitaine” (a regional scientific event organized in Nouvelle-Aquitaine) days were held on Thursday 2 March 2023 at Kedge Business School, organised by Digital Aquitaine and especially the Domex...

Thanks to the chair I hold in Trustworthy AI, I have a number of very interesting and challenging open positions for Masters interships and PhD students.

These positions will be open in january, 2023 for the masters and september, 2023 for the PhDs. If you are interested in Hybrid AI in order to enforce trust on top of black-boxes, please, don’t hesitate to contact me.

Internship 1/3 (February 2023)

Trustworthy AI: Bounding Machine Learning Decisions by Proven Systems (Master + PhD )

In order to achieve the best possible accuracy, decision/recommendation systems built with the help of machine learning act mainly on one lever: the computational complexity of the learned functions (e.g. the number of learned parameters). It is thus common practice to build recommendation systems involving millions of calculations leading to a decision, which has the immediate effect of making it impossible, by construction, to inspect their decision in simple and understandable terms. Aiming solely at precision may have immense applicative interests, but as soon as the implementation requires understanding, justifying, explaining or guaranteeing certain decisions, we see that precision alone is not enough when trust is needed.

In this Master’s internship (followed by a PhD), we propose to study how to bound the behavior of a neural network by two functions, built on some formal language (propositional logic, temporal logic), and adapted to formal verification, in order to mix precision and trust.

The page describing the detailed topic for this internship

The PDF file describing this internship is availaible here

Contact me ASAP if you are interested (contact)

Internship 2/3 (February 2023)

Learning trustworthy systems, a neuro-symbolic approach

In this M2 internship (for which the grant for a PhD is already secured) we propose to study a hybrid approach to machine learning that allows to directly learn functions with structural or semantic properties that would allow to apply, in practice, automatic methods for proof and/or logical reasoning. We will will also study how prior knowledge can be used to converge more quickly or to guarantee that the learned functions respect the properties known in advance.

The page describing the detailed topic for this internship

The PDF file describing this internship is availaible here

Contact me ASAP if you are interested (contact)

Internship 3/3 (February 2023)

Reinforcement learning and Attention Networks for SAT solving

Despite breathtaking results in pattern recognition and signal analysis (sound, image, video, ….) and text, ML / RL-based approaches have not yet succeeded in overtaking conventional methods in solving NP-Hard problems. This class of problems, at the center of theoretical computer science, captures the hardness of a many subfields of AI since its beginnings, such as planning, constraints programming, or even the SAT problem itself (SAT is for “Satisfiability testing”, the main goal of which is precisely to tackle this central problem).

The goal of this Internship (preferably followed by a thesis currently being set up via a CIFRE mechanism) is to study how recent progress allows us to consider the use and extension of ML/RL techniques for the practical resolution of the SAT problem.

The page describing the detailed topic for this internship

Contact me ASAP if you are interested (contact)