Publisher
source

Behzad Bozorgtabar

Just Landed

2 days ago

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

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Nov 1, 2026

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Country

Denmark

University

Aarhus University

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Keywords

Computer Science
Electrical Engineering
Computer Vision
Robotics
Continual Learning
ML
construction planning

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About this position

[PhD fellowship/scholarship; funding information listed as competitive.]

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

The project sits at the intersection of robotics, machine learning, multimodal perception, reasoning, and action. The successful candidate will join the Adaptive & Agentic AI (A3) Lab and work under the supervision of Associate Professor Behzad Bozorgtabar with co-supervision by Professor Qi Zhang.

Research themes include vision-language-action models that connect visual observations and language instructions to robot behaviour; world models and planning for predicting the effect of actions in the physical world; and adaptation and edge intelligence for maintaining reliability under changing environments, sensing conditions, and resource constraints. The project emphasizes new learning algorithms, rigorous evaluation, and original research aimed at top venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, CoRL, RSS, and ICRA.

Applicants should have a master’s degree by enrolment in a relevant field such as computer science, electrical/computer engineering, robotics, or machine learning. Strong foundations in machine learning, linear algebra, probability, and optimisation are expected, along with strong Python and PyTorch skills. The advert specifically requires substantial hands-on experience implementing, training, and evaluating deep-learning models, and evidence of research potential through a thesis, research project, code contribution, or publication.

The position starts from 1 January 2027 or later. Funding is described as competitive, and the post is presented as a PhD fellowship/scholarship.

To apply, use the university's application portal via the Apply button and upload the project description as a PDF, copying the text provided in the advert. The application deadline is 1 November 2026 at 23:59 CET.

Funding details

Available

What's required

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; solid foundations in machine learning, linear algebra, probability, and optimisation; strong Python and PyTorch (or comparable framework) skills; substantial hands-on experience implementing, training, and evaluating deep-learning models; evidence of research potential through a substantial thesis, research project, code contribution, or publication with clearly identified personal technical contribution.

How to apply

Apply through the university homepage using the 'Apply' button. Upload the required project description as a PDF, copied from the provided project text. Submit the full application before 2026-11-01 23:59 CET.

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