Aarhus University
3 days ago
PhD in Physical AI and Adaptive Foundation Models for Robotics at Aarhus University Aarhus University in Denmark
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded Danish PhD stipend for a three-year project. Starting date is around 1 January 2027.
Deadline
Nov 1, 2026
Country
Denmark
University
Aarhus University

How do I apply for this?
Sign in for free to reveal details, requirements, and source links.
Apply for this position
Keywords
Suggested positions
About this position
PhD opportunity at Aarhus University in Physical AI, with a focus on adaptive foundation models for robotics. The project is hosted by the Department of Electrical and Computer Engineering and is described as a three-year, fully funded Danish PhD stipend.
Research themes include developing adaptive foundation models for robotics, moving beyond pretrained APIs, and contributing to core technical AI methods. This is a strong fit for applicants interested in robotics, machine learning, artificial intelligence, and related engineering research.
The post indicates a starting date around 1 January 2027 and an application deadline of 1 November 2026. No detailed eligibility criteria are listed in the post itself beyond the research focus and PhD level.
To apply, use the official jobs.ac.uk posting linked in the post and follow the application instructions there.
Funding details
Fully funded Danish PhD stipend for a three-year project. Starting date is around 1 January 2027.
What's required
Applicants should be interested in a three-year PhD project in Physical AI, adaptive foundation models, and robotics. The post implies strong technical AI skills and research ability; no additional formal requirements are stated in the post.
How to apply
Open the official jobs.ac.uk link for the PhD position and follow the application instructions on the posting. Submit the application before 1 November 2026.
More information can be found here
Ask ApplyKite AI

How do I apply for this?
Sign in for free to reveal details, requirements, and source links.