About the Project We invite applications for a fully funded PhD position in a new interdisciplinary research group led by
Florian Kogelbauer at KAUST. The group combines nonlinear dynamical systems, applied mathematics, scientific computing and machine learning to uncover hidden structures in complex systems and build more accurate, interpretable and efficient models. Why do high-dimensional neural networks often learn surprisingly simple representations? This project investigates the mathematical foundations of machine learning through nonlinear dynamics, invariant manifolds and spectral methods. Research topics include the geometry of neural-network training, implicit regularization, low-rank structures, model reduction, and the design of more efficient and interpretable learning algorithms. What we offer Fully funded PhD position with competitive compensation Access to state-of-the-art high-performance computing and GPU infrastructure A research-intensive environment with minimal teaching responsibilities Opportunities for international collaborations, conferences and research visits A modern, international campus on the Red Sea Who should apply We welcome highly motivated candidates with strong quantitative backgrounds in mathematics, physics, computer science, engineering, statistics, scientific computing or related disciplines. Candidates should show mathematical curiosity, analytical ability and an interest in combining theoretical research with computational methods. Strong programming skills are particularly valuable. Prior expertise in every topic is not required. How to apply Interested candidates should send a CV, a short statement of research interests indicating this project, academic transcripts, and details of potential academic referees. Start date: Fall 2027 Contact: Florian Kogelbauer, kopablauer@icloud.com
