Research Internship in AI Safety and Explainability

il y a 1 semaine


Grenoble, Auvergne-Rhône-Alpes, France CEA Temps plein
Job Description

The CEA List institute is seeking a research intern to work on a project related to AI safety and explainability. The successful candidate will have the opportunity to work on a cutting-edge project that aims to develop methods for generating explanations of AI model behavior.

The internship will involve studying the use of AISER's OOD-detection method, PARTICUL, to identify whether new inputs were tampered with. The candidate will work using the open-source library CaBRNet, developed at CEA LIST, which provides an implementation of PARTICUL.

The broad internship goals are:

  • Familiarization with the state-of-the-art on XAI, OOD-detection, and adversarial examples;
  • Getting started with the PARTICUL implementation in CaBRNet;
  • Design and implementation of benchmarks involving the tampering of whole datasets with adversarial examples;
  • Evaluation against other OOD-detection methods using the Open-OOD benchmark.

The ideal candidate will have a strong background in machine learning and/or statistics, and experience with machine learning theory. The internship will be conducted in a team environment, and the candidate will have the opportunity to work with experienced researchers in the field.

Requirements:

  • Master student or equivalent in computer science;
  • Ability to work in a team;
  • Some knowledge of version control.

Preferred:

  • Formal training in machine learning and/or statistics;
  • Experience with machine learning theory.


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