Postdoc Position in Neuro-morphic Reinforcement Learning

University of Southern Denmark (SDU); Denmark; On-site; DKK 313,432 - 417,910; Full time

Job summary

The University of Southern Denmark (SDU) is inviting applications for a postdoctoral research fellowship in neuro-morphic reinforcement learning, set to begin no later than October 1, 2026. This role involves advancing reinforcement learning within high-dimensional systems, with a strong emphasis on theoretical and algorithmic development.

Successful candidates will have a PhD, a proven publication track record in leading ML conferences, and outstanding programming skills. This position requires collaboration in the ADIN Lab and contributions to teaching responsibilities.

Qualifications

• A PhD in the relevant field at the time of employment.
• At least two first-author research papers in top-tier ML venues.
• Deep understanding of reinforcement learning foundations.

Responsibilities

• Advance algorithmic and theoretical foundations of reinforcement learning.
• Prove theoretical guarantees for control tasks in adaptive systems.
• Collaborate and publish in top-tier venues.

Skills

Reinforcement learning foundations

Scientific programming (Python, PyTorch/JAX)

Communication skills in English

Education

PhD in Computer Science, Mathematics, Statistics, or Theoretical Physics

Tools

Python

PyTorch

JAX

Job description

The SDU Adaptive Intelligence Lab (ADIN Lab) (https://adinlab.github.io/) 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

• 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.

Application deadline

31 August 2026 at 23:59 hours local Danish time

Please see the full call, including how to apply, on www.sdu.dk