PhD Position in Physical AI: Adaptive Foundation Models for Robotics
Explore cutting-edge robotics AI at Aarhus University’s Adaptive & Agentic AI Lab, developing adaptive foundation models for real-world robotic applications.
Best for:Best for PhD candidates with a strong background in machine learning, robotics, or computer science, hands-on experience with deep learning frameworks (PyTorch), and a passion for developing adaptive AI systems for real-world robotic applications.
Overview
This PhD position at Aarhus University’s Department of Electrical and Computer Engineering focuses on advancing **Physical AI**—specifically, adaptive foundation models for robotics. The successful candidate will join the **Adaptive & Agentic AI (A3) Lab**, supervised by Associate Professor Behzad Bozorgtabar and Professor Qi Zhang. The project aims to bridge multimodal perception, reasoning, and action in robots, enabling them to understand instructions, predict physical outcomes, and adapt dynamically to changing environments. Research will emphasize **vision-language-action models (VLAs)**, **world models for planning**, and **edge intelligence**, with a strong emphasis on rigorous evaluation using public datasets, simulations, and robotic platforms. The goal is to publish high-impact research in top-tier machine learning, computer vision, and robotics venues like NeurIPS, ICML, and CVPR.
What you'll do
- Develop and investigate vision-language-action models (VLAs) that connect visual observations, language instructions, and robotic behavior.
- Design predictive world models to simulate physical interactions and support robotic planning and learning from demonstrations.
- Explore adaptive methods for maintaining reliable robotic behavior under changing environments, sensing conditions, and resource constraints.
- Conduct rigorous evaluations using public datasets, simulations, and robotic/edge-computing platforms, focusing on task success, generalization, reliability, and computational efficiency.
- Publish original research in top-tier machine learning, computer vision, and robotics conferences (e.g., NeurIPS, ICML, CVPR, CoRL).
- Collaborate closely with supervisors to define and refine the research focus within the broader project objectives.
Requirements
Degree 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.
Degree offered: PhD
Language: English (no specific proficiency test mentioned)
Instruction: English
Publications: Original, high-quality research targeting top-tier conferences in machine learning, computer vision, and robotics.
- Strong academic background in machine learning, linear algebra, probability, and optimization.
- Proficient Python and PyTorch (or comparable deep learning framework) programming 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 a clear technical contribution.
- Foundational knowledge of robotics, computer vision, and AI principles.
- Applicants must have completed their master’s degree by the time of enrolment.
- No explicit nationality or residency restrictions mentioned.
Funding & terms
How to apply
Deadline: 2026-11-01 (CET)
Applicants must submit their application through the university’s official portal. A project description (PDF) must be uploaded, which should be a copy of the project description provided in the job posting. Ensure all required documents, including CV, transcripts, and evidence of research potential, are included.
- CV
- Project description (PDF, copied from the job posting)
- Master’s degree transcripts
- Proof of research potential (e.g., thesis, research project, code contribution, or publication)
- Cover letter
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