Aarhus University
4 months ago
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PhD fellowship
PhD in Efficient Test-Time Model Adaptation in Dynamic Edge Environments Aarhus University in Denmark
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded PhD position/fellowship at Aarhus University. Salary and terms of employment are in accordance with the applicable collective agreement. No stipend amount is stated.
Deadline
Expired
Country
Denmark
University
Aarhus University
Ongoing programGoogle PhD Fellowship Program
by Google
Google PhD Fellowship Program supports outstanding PhD students conducting innovative research in computer science and closely related fields through university nomination.
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Keywords
About this position
PhD position in Efficient Test-Time Model Adaptation in Dynamic Edge Environments at Aarhus University, Denmark, in the Department of Electrical and Computer Engineering and the Adaptive & Agentic AI (A3) Lab.
The project sits at the intersection of foundation models, edge intelligence, machine learning, computer vision, and real-time adaptive AI. The research aims to build high-performance, low-latency test-time adaptation methods for unimodal and multimodal models operating in dynamic edge environments where data streams face domain shifts, hardware degradation, and changing physical conditions.
The successful candidate will work under Dr. Behzad Bozorgtabar (main supervisor) and Prof. Qi Zhang (co-supervisor). The project emphasizes autonomous monitoring, uncertainty estimation, on-the-fly adaptation, efficiency, and reliability for mission-critical applications such as autonomous robotics and industrial monitoring.
Funding: fully funded PhD fellowship/scholarship. Salary and employment terms follow the applicable collective agreement. No stipend amount is specified.
Eligibility: applicants must hold a master’s degree (120 ECTS) in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related quantitative field. Strong Python and PyTorch skills are expected, together with a solid background in machine learning and/or computer vision. Experience with modern neural networks and edge-specific model compression techniques is advantageous.
Application materials: statement of interest (1 page), CV including publication list and technical portfolio, academic transcripts and diplomas, and a project description copied from the announcement and uploaded as a PDF.
Deadline: 15 August 2026 at 23:59 CEST. Preferred start date is 01 November 2026.
Applicants can submit via the official application portal linked in the announcement. For questions, contact [email protected] or [email protected].
Funding details
Fully funded PhD position/fellowship at Aarhus University. Salary and terms of employment are in accordance with the applicable collective agreement. No stipend amount is stated.
What's required
Applicants must hold a master’s degree (120 ECTS) in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related quantitative field. Strong proficiency in Python and deep learning frameworks such as PyTorch is required, along with a solid foundation in machine learning and/or computer vision. Interest in test-time adaptation, autonomous AI systems, and edge intelligence is expected. Familiarity with modern neural networks is required, and experience with edge-specific model compression, knowledge distillation, lightweight design, or parameter-efficient fine-tuning is highly advantageous. Applicants should value reproducibility, open-source contribution, and working across algorithmic AI and practical edge systems.
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Professors
Associate Professor at Aarhus University
Denmark
Research Interests
Statistics
Artificial Intelligence
Electrical Engineering
Computer Vision
Computer Science
Professor at Aarhus University
Denmark
Research Interests
Internet Of Things
Cloud Computing
Computer Science
Electrical Engineering
Deep Learning
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