Umeå University welcomes applications for a tenure track position as Assistant Professor in Machine Learning. The position, which is established through the Wallenberg AI, Autonomous Systems and Software Program (WASP, http://wasp-sweden.org/), comes with a substantial recruitment package.
The primary focus for this position is research on advanced machine learning techniques. The successful applicant can request to be affiliated with the Department of Mathematics and Mathematical Statistics which has an expansive research program on the mathematical foundations of artificial intelligence, including the mathematics of deep learning, statistical learning with sparsity, optimization, and compressive sensing.
Your expertise should primarily be within the core areas of machine learning as e.g.:
Data representation learning
Explainability and interpretability of AI/ML systems
Incremental learning, and multi-task/transfer learning
Interaction between AI, machines, humans and society
Learning methods for estimating robustness, stability and reliability of AI/ML systems
Learning with non-convexity (e.g., GANs, sequential learning, reinforcement learning)
The successful candidate will have an important role in developing the area of machine learning at the department. The candidate is also expected to develop collaboration across departmental boundaries inside and outside the University and within WASP.
The position is for 5 years, of which 80 % is for research and and 20 % for pedagogical qualification/teaching. It includes a substantial recruitment package including full funding of two PhD students and two postdocs. Applicants with a background in mathematics or statistics are welcome, as well as applicants from other fields close to machine learning.
A successful applicant who requests to be affiliated with the Department of Mathematics and Mathematical Statistics will also benefit from extensive departmental support that can, for example, include extra start-up funds and co-funding of additional PhD students.
Informal enquiries are encouraged and should be directed to Professor Jun Yu (email@example.com).
For more details and instructions on how to apply, see
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