Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position is available from 01 November 2026 or later. You can submit your application via the link under 'how to apply'.
Title
PhD Position in Efficient Test-Time Model Adaptation in Dynamic Edge Environments
Research area and project description
Applications are invited for a fully funded PhD position within the Department of Electrical and Computer Engineering at Aarhus University. The successful candidate will be integrated into the A3 Lab – Adaptive & Agentic AI, directed by Dr. Behzad Bozorgtabar, who serves as the primary supervisor. This doctoral research is co-supervised in close collaboration with Prof. Qi Zhang, offering a unique interdisciplinary research environment at the intersection of Foundation Models and Edge Intelligence.
Research Vision. Deploying models in edge environments requires navigating a fundamental conflict between model complexity and environmental volatility. Real-world edge environments remain highly dynamic: data streams are continuously subject to "domain shifts" caused by fluctuating conditions, hardware degradation, or changing physical surroundings.
Traditional AI models are often brittle under these distribution shifts, leading to unreliable outputs that can compromise safety in mission-critical applications—ranging from autonomous robotics to real-time industrial monitoring. To maintain performance without the latency penalties of cloud-based recalibration, edge AI systems must become "self-aware" and capable of autonomous evolution.
Core Research Objectives. The primary objective of this PhD is to develop a high-performance, low-latency framework for Test-Time Adaptation (TTA). This involves designing autonomous architectures capable of monitoring and maintaining the reliability of unimodal and multimodal foundation models in real-time. Key research pillars include:
- Autonomous Monitoring: Developing mechanisms to detect distribution shifts and quantify model uncertainty across heterogeneous data types.
- On-the-Fly Adaptation: Designing lightweight TTA algorithms that can recalibrate models at the edge under strict latency and computational constraints.
- Efficiency and Reliability: Balancing the trade-offs between adaptation accuracy, energy efficiency, and hard real-time execution.
Project description
For technical reasons, you must upload a project description. Please simply copy the project description above and upload it as a PDF in the application.
Qualifications and specific competences
Applications to the PhD position must hold a master’s degree (120 ECTS) in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related quantitative field.
Further qualifications:
- Technical Skills: Advanced proficiency in Python and deep learning frameworks (e.g., PyTorch).
- Core Knowledge: A strong foundation in machine learning and/or computer vision. The candidate should have a specific interest in test-time adaptation, autonomous AI systems and edge intelligence.
- Advanced Architectures & Edge AI: Familiarity with modern neural networks is required. Experience with edge-specific model compression—such as knowledge distillation, lightweight design, or parameter-efficient fine-tuning—is highly advantageous.
- Attributes: A mindset for reproducibility, open-source contribution, and the ability to work across the boundaries of algorithmic AI and practical edge systems.
Application Requirements (How to Apply) Please ensure your application includes the following documents:
- Statement of Interest (1 page): Detailing your background in ML/CV, any work experience relevant to the position (if applicable), and your motivation for joining the A3 Lab.
- Curriculum Vitae: Including a publication list (if applicable) and technical project portfolio.
- Academic Records: Transcripts and diplomas (Bachelor’s and Master’s).
- Project Description Requirement: The candidate is required to use the project description from the PhD announcement.
Place of employment and place of work
The place of employment is Aarhus University, and the place of work is Adaptive & Agentic AI (A3) Lab, Department of Electrical and Computer Engineering (ECE), Faculty of Technical Sciences, Aarhus University, Finlandsgade 22, 8200 Aarhus N., and Denmark Research Centre Flakkebjerg, Forsøgsvej 1, DK-4200 Slagelse, Denmark.
Contacts
Applicants seeking further information regarding the PhD position are invited to contact:
- Behzad Bozorgtabar, behzad@ece.au.dk (main supervisor)
- Qi Zhang, qz@ece.au.dk (co-supervisor)
For information about application requirements and mandatory attachments, please see our application guide. If answers cannot be found there, please contact:
How to apply
Please follow this link to submit your application.
Application deadline is 15 August 2026 at 23:59 CEST.
Preferred starting date is 01 November 2026.
Please note:
- Only documents received prior to the application deadline will be evaluated. Thus, documents sent after deadline will not be taken into account.
- The programme committee may request further information or invite the applicant to attend an interview.
- Shortlisting will be used, which means that the evaluation committee only will evaluate the most relevant applications.
Aarhus University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants. All interested candidates are encouraged to apply, regardless of their personal background. Salary and terms of employment are in accordance with applicable collective agreement.
INFORMATIONER OM STILLINGEN:
- Arbejdspladsen ligger i:
Slagelse Kommune
-Virksomheden tilbyder:
-Arbejdsgiver:
Aarhus Universitet, Finlandsgade, 4200 Slagelse
-Ansøgning:
Ansøgningsfrist: 12-08-2026;
Se mere her: https://job.jobnet.dk/find-job/8073b737-3941-47e6-8482-cdd74d01ea17
