PhD Position in Physical AI: Adaptive Foundation Models for Robotics

University's Department of Electrical and Computer Engineering. The candidate will join the Adaptive & Agentic AI (A3) Lab, supervised by Associate Professor Behzad Bozorgtabar and co-supervised by Professor Qi Zhang. Research vision How can intelligent robots understand instructions, anticipate the consequences of actions, and adapt reliably when the physical world changes? The project connects multimodal perception, reasoning and action with predictive learning and edge intelligence. Research directions and objectives Vision-language-action models (VLAs) Investigate VLA models and multimodal representations that connect visual observations and language instructions to robot behaviour, including learning from demonstrations and generalisation to unfamiliar tasks, objects or environments. World models and planning Develop models that predict how the physical world responds to actions, supporting planning and learning from interaction. Possible directions include learning from video, demonstrations and simulation, and transferring knowledge across robot configurations. Adaptation and edge intelligence Develop efficient methods for maintaining reliable behaviour under changing environments, sensing conditions and resource constraints. Topics may include test-time and continual adaptation, uncertainty-aware decision-making, and efficient inference and model updates under latency, memory and energy limits. The precise research focus will be developed with the successful candidate within these connected directions. Research will emphasise new learning algorithms and rigorous evaluation using public datasets, simulation and, where available, robotic and edge-computing platforms. Evaluation will consider task success, generalisation, reliability and computational efficiency. The goal is original research for leading machine-learning, computer-vision and robotics venues. The successful candidate will be expected to conduct original, high-quality research targeting publications at top-tier machine learning, computer vision and robotics conferences, including NeurIPS, ICML, ICLR, CVPR, ICCV, CoRL, RSS and ICRA. Project description For technical reasons, you must upload a project description. Please simply copy the project description above and upload it as a PDF in the application. Qualifications and specific competences: Essential qualifications A master’s degree (120 ECTS or equivalent), completed by enrolment, in computer science, electrical or computer engineering, robotics, machine learning or a related field; strong academic results and foundations in machine learning, linear algebra, probability and optimisation; and strong Python and PyTorch (or comparable framework) skills. Only applicants who demonstrate substantial hands-on experience implementing, training and evaluating deep-learning models will be considered. General interest in AI or experience limited to running tutorials or using pretrained-model APIs is not sufficient. Applicants must provide evidence of research potential through a substantial thesis, research project, code contribution or publication, clearly identifying their own technical contribution.

Application deadline

01 November 2026 at 23:59 CET.

Preferred starting date is 01 January 2027.

Please read the full job description and apply at the university homepage via the 'Apply' button above.

Competitive