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Aarhus University
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PhD Position in Efficient Test-Time Model Adaptation in Dynamic Edge Environments Aarhus University in Denmark
Degree Level
PhD
Field of study
Computer Science
Funding
Full funding availableDeadline
Aug 15, 2026
Country
Denmark
University
Aarhus University

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About this position
PhD opportunity at Aarhus University in the Department of Electrical and Computer Engineering for research on efficient test-time model adaptation in dynamic edge environments. The project focuses on artificial intelligence, computer vision, machine learning, and edge AI, with an emphasis on making unimodal and multimodal foundation models robust under changing real-world conditions.
The research addresses distribution shifts caused by changing surroundings, sensor degradation, hardware constraints, and evolving data streams. The goal is to develop lightweight methods for autonomous monitoring, on-the-fly adaptation during inference, efficient foundation-model adaptation, and reliability under strict latency, memory, and energy limits on edge devices.
The successful candidate will join the Adaptive & Agentic AI (A3) Lab and be supervised by Associate Professor Behzad Bozorgtabar with co-supervision by Professor Qi Zhang. The environment is described as international and collaborative, with opportunities to publish at venues such as NeurIPS, ICLR, and CVPR.
Eligibility highlights include a Master’s degree equivalent to 120 ECTS in computer science, computer engineering, electrical engineering, machine learning, or a closely related quantitative field. Applicants should have strong Python skills and experience with PyTorch. Helpful background includes test-time or domain adaptation, foundation models, multimodal learning, model compression, parameter-efficient fine-tuning, and resource-efficient inference for edge hardware.
This is a fully funded PhD project open to students worldwide. The application deadline is 15 August 2026 at 23:59 CEST. Interested applicants should register their interest via the FindAPhD enquiry form linked in the post.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
How to apply
Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.
More information can be found here
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