Thanks to the funding from the Trustworthy AI chair, I have several very interesting funding opportunities for master’s internships. Additionally, thanks to a Google Award received at the end of 2024, I also have a research engineer position and research internships available around SAT and the parallelization of constraint solving.
These offers will be open in January 2025. If you are interested in hybrid AI to enhance trust in black-box machine learning systems, feel free to contact me.
I also have the possibility to offer internships (and sometimes PhDs) on topics at the heart of SAT research.
Parallelization CSP / SAT (Master’s Internship)
Start Date: February 2025
SAT, Logic, Constraint Programming, Parallelization
Location: LaBRI, Bordeaux Computer Science Research Laboratory, Talence, France
(This Master’s internship is funded by the Google Award 2024)
Summary of the proposed master’s project (see link for detailed topic)
This Master’s internship aims to explore the parallelization of SAT solvers, focusing on formulas derived from constraint-expressed problems. Despite advances in solving SAT problems, leveraging modern parallel architectures remains a challenge. The internship aims to study how to optimize task sharing to improve solving efficiency, particularly by judiciously selecting sub-problems to be addressed based on available resources. Candidates should have strong knowledge in Programming.
Mitigating Bias in LLMs (Master’s Internship)
Start Date: February 2025
SAT, Logic, Constraint Programming, Parallelization
Location: LaBRI, Bordeaux Computer Science Research Laboratory, Talence, France
(This Master’s internship is funded by the Google Award 2024)
Summary of the proposed master’s project (see link for detailed topic)
This Master’s internship at LaBRI, funded by the “Trustworthy AI” chair, focuses on studying biases in text generation by large language models. While advances in machine learning open up new applications, understanding and explaining the decisions made by these models remain obstacles to their adoption, especially in critical contexts. The internship aims to establish a state of the art on biases present in these systems, focusing on specializing a model to reduce biases without human supervision. Candidates should have strong knowledge in machine learning, symbolic AI, and good programming skills.