The Centre for Machine Learning located under the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark invites applications for a paid PhD research fellowship position within the field of probabilistic machine learning to be filled earliest by 1 January 2026 for a period of three years. 

The research project will investigate new algorithmic principles that make learning agents adapt to non-stationary environments in an autonomous manner. The expected outcomes are new theoretical insights about the algorithmic roots of autonomous behavior and their representative demonstrations in challenging simulation environments.  

IMADA has the unique feature of bringing mathematicians and computer scientists together within a single department to foster theoretically well-backed high-quality data science research. IMADA is home to many ongoing externally funded research projects, as well as to a rich curriculum of data science courses. Data Science and Statistics Group is a synergy platform for the data science experts in IMADA.

 We are seeking candidates with strong desire to make significant contributions to science, specific interest in fundamental machine learning research, and an outstanding theoretical background in probabilistic machine learning and reinforcement learning demonstrated by excellent course grades and by scientific publications at major international peer-reviewed venues of core machine learning research. Strong scientific programming skills demonstrated by contributions to public code repositories or released source code from the candidate’s own publications is a big plus. We also expect excellent spoken and written communication skills in English.

The successful candidate will join the vibrant research atmosphere at the SDU Adaptive Intelligence Laboratory and entertain substantial peer support in both technical and social matters. The candidate will contribute to the publication of high-quality research papers at top-tier machine learning venues such as NeurIPS, ICML, AISTATS, together with the other lab members. The candidate will also fulfill teaching assistantship duties. 

We will consider the candidates who have a Master's degree in Computer Science, Mathematics, Statistics, or Theoretical Physics at the time of the commencement of employment. Candidates should demonstrate that they have passed at least two Master’s level courses that cover advanced machine learning topics with a grade that corresponds to top 10% success within the native grade scale.