Internship Offer at GIPSA-lab: Control-Theoretic Enhancements for Gradient-Based Neural Network Training

Candidate Profile The ideal candidate is a motivated Master’s student or early-stage PhD in Computer Science, Applied Mathematics, Automatic Control, or a related discipline. They should have: • Good foundations in optimization, machine learning, and control theory. • Experience in Python programming and deep learning frameworks. • Analytical skills to study algorithmic stability and convergence. • Curiosity and independence, with the ability to design experiments and interpret results. Prior experience in online learning, adaptive control, or accelerated optimization is a plus, but not strictly required