Summary
Aarhus University is inviting applications for a fully funded PhD fellowship within the Graduate School of Technical Sciences. The successful candidate will conduct research on Test-Time Adaptation for Edge Intelligence, participating in a unique interdisciplinary environment.
PhD Position in Agentic Test-Time Adaptation for Efficient and Reliable Edge Intelligence, Aarhus University, Denmark
Designation
PhD Candidate
Table
| Field | Details |
|---|---|
| Research Title | Agentic Test-Time Adaptation for Efficient and Reliable Edge Intelligence |
| Research Area | Electrical and Computer Engineering (AI, Edge Intelligence) |
| Location | Aarhus University, Denmark |
| Eligibility/Qualifications | Master’s degree in relevant fields, advanced proficiency in Python and deep learning frameworks |
| Application Deadline | 20 May 2026 at 23:59 CEST |
| Starting Date | 01 August 2026 or later |
Research Area
The project focuses on developing a high-performance, low-latency framework for Test-Time Adaptation (TTA) aimed at making AI systems more robust and self-aware, particularly in dynamic edge environments.
Location
Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark.
Eligibility/Qualification
- Master’s degree (120 ECTS) in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related quantitative field.
- Advanced proficiency in Python and deep learning frameworks (e.g., PyTorch).
- Strong foundation in machine learning and computer vision, with specific interest in Test-Time Adaptation, Continual Learning, and autonomous AI systems.
- Familiarity with advanced neural network architecture, model compression techniques, and a mindset for reproducibility is advantageous.
Job Description
The PhD candidate will work on:
- Developing autonomous architectures for monitoring distribution shifts in real-time.
- Designing lightweight TTA algorithms to adapt models under strict constraints.
- Balancing trade-offs between adaptation accuracy, energy efficiency, and latency.
How to Apply
Interested candidates should submit their application including:
- Statement of Interest (1 page)
- Curriculum Vitae
- Academic Records (Transcripts and diplomas)
- Project description in PDF format
Applications can be submitted through the Aarhus University application link.
Last Date for Apply
20 May 2026 at 23:59 CEST.
For further inquiries, contact:
- Behzad Bozorgtabar at behzad@ece.au.dk
- Qi Zhang at qz@ece.au.dk
