Summary:
Aarhus University invites applications for a fully funded PhD position within the Department of Electrical and Computer Engineering. This role focuses on developing autonomous frameworks for Test-Time Adaptation (TTA) to enhance the reliability of AI models in dynamic edge environments.
PhD Position at the Department of Electrical and Computer Engineering, Aarhus University, Denmark
Designation:
PhD Fellow
Table:
| Details | Information |
|---|---|
| Research Area | Electrical and Computer Engineering |
| Location | Aarhus University, Denmark |
| Eligibility/Qualification | Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related field; proficiency in Python and deep learning frameworks. |
| Salary | As per collective agreement |
| Application Deadline | 20 May 2026, 23:59 CEST |
| Preferred Starting Date | 01 August 2026 |
Research Area:
The PhD position resides within the newly established A3 Lab – Adaptive & Agentic AI. Research focuses on navigating the complexities of deploying Foundation Models in volatile edge environments by developing high-performance, low-latency frameworks for real-time model adaptation.
Location:
Department of Electrical and Computer Engineering (ECE), Faculty of Technical Sciences, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark.
Eligibility/Qualification:
- Master’s degree (120 ECTS) in:
- Computer Science
- Computer Engineering
- Electrical Engineering
- Machine Learning
- Related quantitative fields
- Technical Skills:
- Advanced proficiency in Python and deep learning frameworks (e.g., PyTorch)
- Core Knowledge:
- Strong foundation in machine learning and/or computer vision
- Interest in Test-Time Adaptation, Continual Learning, Machine Unlearning, Foundation Models, or autonomous AI systems
- Advanced Architectures & Edge AI:
- Familiarity with modern neural network architectures and model compression techniques
Job Description:
The successful candidate will:
- Develop mechanisms to autonomously monitor model performance.
- Design lightweight TTA algorithms for edge recalibration under strict constraints.
- Balance adaptation accuracy and energy efficiency.
How to Apply:
Applicants should submit the following documents via the application link provided:
- Statement of Interest (1 page)
- Curriculum Vitae, including publication list (if applicable)
- Academic Records: Transcripts and diplomas (Bachelor’s and Master’s)
Last Date for Apply:
20 May 2026 at 23:59 CEST
For further information, potential applicants can contact:
- Behzad Bozorgtabar (Main Supervisor): behzad@ece.au.dk
- Qi Zhang (Co-Supervisor): qz@ece.au.dk
This opportunity promises a pioneering path in adaptive AI research with avenues for publication in high-tier machine learning and computer vision venues.
