Eloi Martinet

Eloi Martinet is a postdoctoral researcher at the University of Würzburg, affiliated with the Mathematics of Machine Learning team within the Institute of Mathematics. He holds a PhD in Spectral Shape Optimization from LAMA and LJK (2019–2023), an engineering degree from ENSIMAG, and an Agrégation de Mathématiques. His research focuses on variational methods in machine learning, numerical PDEs, and shape optimization using neural networks and graph-based approaches.

Education:

• PhD in Spectral Shape Optimization (2019–2023), LAMA (Chambéry) & LJK (Grenoble)
• Agrégation de Mathématiques (2020–2021), Institut Fourier (Grenoble)
• Engineering School in Informatics and Applied Mathematics (2016–2019), ENSIMAG (Grenoble)

Research Interests: Shape/topology optimization, numerical methods for PDEs, level set methods, neural networks, and graph-based machine learning. His work explores variational approaches to machine learning and solving PDEs with neural networks.

Teaching: Recent roles include lectures on Data Science foundations, Machine Learning on Graphs, and numerical analysis tutorials at JMU and ENSIMAG.

Grants & Supervision: Co-supervised MSc internships on phase fields on graphs and PDE spectrum analysis. His research projects include optimizing Neumann eigenvalues on spheres and meshless shape optimization with neural networks.

Labs/Teams: Member of the Mathematics of Machine Learning team at the University of Würzburg, collaborating on projects involving FreeFem and Python tools for eigenvalue optimization.