PhD Position in Foundations of Mathematical Optimization for Artificial Intelligence, SURE-AI

We are pleased to announce a new PhD Research Fellowship in the Department of Numerical Analysis and Scientific Computing at Simula Research Laboratory (Simula) for a highly-motivated and self-driven PhD student with strong intellectual curiosity and interest in the mathematical foundations of artificial intelligence. The position is part of the National Norwegian Center for Sustainable, Risk-averse and Ethical AI (SURE-AI), funded by the Research Council of Norway (RCN) (2025-2030), coordinated by Simula Research Laboratory (Simula). Simula is a leading Norwegian research institute known for its excellence in cutting-edge ICT with a strong track record of top evaluations, active international collaborations, and success in significant funding initiatives, including European and National RCN grants. Simula is committed to supporting talent through a variety of PhD and postdoctoral programs and provides valuable research infrastructure, such as the national HPC facility, eX3. Job Description We are looking for a PhD candidate who will be part of an interdisciplinary research environment. At Simula, the candidate will work in the research team led by Thomas M. Surowiec, which will include a postdoctoral fellow working on similar topics and two international adjunct professors. The PhD candidate will also be expected to be enrolled in the PhD program at the Department of Mathematics at the University of Oslo, a premier European research environment that combines a rich academic heritage with a forward-looking focus on innovation in pure and applied mathematics. A strong background in mathematics is essential. The successful PhD candidate is expected to carry out research on one or more of the following general focus areas: Adaptivity and algorithmic fine-tuning techniques. Large-scale numerical optimization algorithms for risk-averse training. Quantitative stability and distributional robustness of stochastic programs. Asymptotic behavior of predictive energy landscapes in dynamic environments Desired Skill Sets and Competencies criteria We will consider candidates who have completed (or will have completed by June 2026) a Master’s degree (or equivalent) in Mathematics, Applied Mathematics, Computational Science, or Physics. The successful candidate will have a solid theoretical foundation in one or more of the topics: Optimization, Real & Functional Analysis, Probability Theory or Numerical Analysis. More specifically, we are looking for candidates with competency in at least one of the following areas and a willingness to learn the others: Stochastic Algorithms & Adaptivity Risk-Averse Optimization and Distributionally Robust Optimization Quantitative Stability Analysis for Stochastic Programs Variational Analysis Proficiency in a high-level programming language (e.g., Python, Julia, C++). The purpose of the fellowship is research training leading to the successful completion of a PhD degree. Please read the PhD program information and the specific grade and language admission requirements.