Complétez votre profil pour recevoir des offres adaptées.

Mon espace

Mes CV vus

Mes candidatures

Mes alertes

Mon profil

Paramètres

Déconnexion

Missions d'intérim
Offres de stage
Offres en alternance
Créer mon alerte
Déposer mon CV
Salaire brut net

Téléchargez l'app et postulez dans les premiers !

Diffuser ma première offre Déjà client

Téléchargez l'app et postulez dans les premiers !

Se connecter S'inscrire Formation

Téléchargez l'app et postulez dans les premiers !

Trouver mon job s
Trouver mon entreprise s
Accès recruteur
- Diffuser ma première offre
- Déjà client
Télécharger l’app

Emploi
- Missions d'intérim
- Offres de stage
- Offres en alternance
- Créer mon alerte
- Déposer mon CV
- Salaire brut net
Formation
Emploi
Emploi
Mon compte
- Se connecter Mon compte
- S'inscrire
- Mon espace
- Mes CV vus
- Mes candidatures
- Mes alertes
- Mon profil
- Paramètres
- Déconnexion

M1 - M2 Internship In Computer Science - Artificial Intelligence - Machine Learning M - F H/F CEA

Palaiseau - 91
Stage
Bac +3, Bac +4
Industrie high-tech • Telecom

Lire dans l'app

Les missions du poste

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.

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.