Up to two postdoc positions in the Mathematical Foundations of Artificial Intelligence group at Umeå University, under the supervision of WASP Assistant Professor Alp Yurtsever.
Project description and working tasks
The connection between optimization and machine learning is at the heart of many recent breakthroughs in artificial intelligence and autonomous systems. Many machine learning applications are modeled as optimization problems, and as problems become more and more complex, new optimization methods are needed. Within this framework, the successful candidate is encouraged to develop their research agenda in close collaboration with their supervisor. Potential areas of interest include, but are not limited to:
- Efficient and scalable algorithms for machine learning,
- Optimization problems with stochastic constraints,
- Implicit regularization in neural networks,
- Optimization with quantum computation,
- Distributed optimization and federated learning,
- Operator splitting methods in optimization,
- Continual learning,
- Bilevel optimization and applications in machine learning,
- Representation learning,
- Optimization on manifolds.
The project is part of the AI/MLX track of the Wallenberg AI, Autonomous Systems, and Software Program (WASP).