PhD Position in Physical AI: Adaptive Foundation Models for Robotics Aarhus University in Denmark

[Three-year PhD fellowship/scholarship; funding is described as competitive.]

Aarhus University is offering a three-year PhD fellowship/scholarship in Physical AI: Adaptive Foundation Models for Robotics, within the Department of Electrical and Computer Engineering at the Graduate School of Technical Sciences, Denmark.

The successful candidate will join the Adaptive & Agentic AI (A3) Lab and work under the supervision of Associate Professor Behzad Bozorgtabar, with Professor Qi Zhang as co-supervisor. The project sits at the intersection of multimodal perception, reasoning, action, predictive learning, and edge intelligence, with a strong emphasis on original research for leading machine learning, computer vision, and robotics venues.

Research directions include vision-language-action models (VLAs), world models and planning, and adaptation/edge intelligence. More specifically, the project explores how robots can connect visual observations and language instructions to behaviour, learn from demonstrations and generalise to unfamiliar tasks, objects, or environments; predict how the world changes in response to actions; and remain reliable under changing conditions, limited resources, and uncertain environments. Potential topics include learning from video, simulation, and demonstrations, test-time and continual adaptation, uncertainty-aware decision-making, and efficient inference and model updates under latency, memory, and energy constraints.

The candidate will be expected to carry out rigorous experimental research using public datasets, simulation, and where available robotic and edge-computing platforms. Evaluation will focus on task success, generalisation, reliability, and computational efficiency. The project description must be uploaded as a PDF; applicants are instructed to copy the project description text provided in the advert into the file.

Eligibility highlights include a master’s degree (or equivalent) completed by enrolment in computer science, electrical/electronic engineering, robotics, machine learning, or a related field; strong preparation in machine learning, linear algebra, probability, and optimisation; and strong Python and PyTorch skills. Demonstrated hands-on experience building, training, and evaluating deep-learning models is essential, as is evidence of research potential through a thesis, project, code contribution, or publication.

The position is available from 01 January 2027 or later. The application deadline is 01 November 2026 at 23:59 CET. Funding is listed as competitive, and the post is presented as a PhD fellowship/scholarship at Aarhus University in Denmark.