Postdoc Position in Neuro-morphic Reinforcement Learning

The SDU Adaptive Intelligence Lab (ADIN Lab) () 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 postdoctoral research fellowship position within the field of neuro-morphic reinforcement learning to be filled earliest by 1 October 2026 for a period of two years.

About the Project
The successful candidate will advance the algorithmic and theoretical foundations of reinforcement learning applied to complex, high-dimensional dynamical systems. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust and adaptive control under uncertainty and non-stationarity. The postdoc will be responsible for proving theoretical guarantees (e.g., convergence, stability, or sample complexity) for control tasks in non-stationary environments with application to adaptive robotic systems and embodied AI, while translating these insights into scalable, high-fidelity simulation implementations.

Research Environment
IMADA uniquely brings mathematicians and computer scientists together within a single department to foster theoretically well-backed, high-quality data science research. The department is home to numerous externally funded research projects, and the Data Science and Statistics Group serves as a vibrant synergy platform for experts across fields. The successful candidate will join the ADIN Lab, collaborate on publishing at top-tier venues (NeurIPS, ICML, ICLR, AISTATS), and fulfill standard teaching assistantship duties.

Expected Skills and Qualifications
We are seeking a candidate with a strong desire to make significant contributions to fundamental machine learning research, possessing a combination of mathematical maturity and advanced engineering skills:

• Education: A PhD in Computer Science, Mathematics, Statistics, or Theoretical Physics at the time of employment.
• Publication Track Record: At least two first-author research papers at flagship venues of core machine learning research (e.g., NeurIPS, ICML, ICLR, AISTATS).
• Theoretical Rigor: A deep understanding of reinforcement learning foundations, with the ability to perform convergence and finite-sample analysis of complex, non-linear continuous control algorithms.
• Implementation Expertise: Outstanding scientific programming skills (Python, PyTorch/JAX) with a proven track record of developing, debugging, and scaling complex RL pipelines or custom simulation environments. Clean public repositories or released source code from past publications is a strong plus.
• Algorithmic Breadth: Familiarity with probabilistic machine learning, distributional reinforcement learning, or bio-inspired neural architectures is highly desirable.
• Communication: Excellent spoken and written communication skills in English.

Please see the full call, including how to apply, on