M1/M2 Internship in Computer Science / Artificial Intelligence / Machine Learning (M/F)

Description de l'offre : This internship is designed for Master 1 students (duration 3 to 4 months), but can be extended (duration 6 months) for Master 2 students. The main objective of this internship is to introduce the notion of Hierarchical Classification in CAISAR, and use it to verify AI systems with PyRAT. The main steps are: to define a file format to represent the hierarchy of classes to modify how the provers are called by CAISAR and their outputs to determine when the AI system is abstract safe to experiment on at least one case study (e.g. CIFAR-10) if the intership is exended, there will also be a theoretical part (Abstract Interpretation framework) CAISAR is an open-source platform that focuses on the characterization of AI systems' Robustness and Safety. In order to ensure the safety of an AI system, this platform can call several provers including PyRAT, a Python tool based on Abstract Interpretation techniques also developed at CEA in the AISER team. Those two tools are under active development, as new features are added to improve their accuracy and the expressivity of their specification language. CaiSAR is written in Ocaml, PyRAT is written in Python3. This work will have contributions to the field of automata learning and to neural networks verification. The internship will likely conclude by publishing a paper (workshop, conference) depending on the quality of the work to be carried.

Profil du candidat : The candidate will work at the crossroads of formal methods and machine learning. As it is not realistic to be expert in both fields, we encourage candidates that do not meet the full qualification requirements to apply nonetheless. Minimal Requirements Master 1 student or equivalent (2nd engineering school year) in computer science or applied mathematics knowledge of at least one programming language ability to work in a team fluent in French or English Preferred Requirements knowledge of OCaml and Python notions of abstract interpretation and/or formal methods We strive to provide an inclusive and enjoyable workplace. We are aware of discriminations based on gender (especially prevalent on our fields), race or disability, we are doing our best to fight them.

Langues : Anglais Courant