Publisher
source

Aarhus University

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 flag

Country

Denmark

University

Aarhus University

Ongoing program

Google PhD Fellowship Program

by Google

Partially Funded
PhD
Up To 3 Years

Google PhD Fellowship Program supports outstanding PhD students conducting innovative research in computer science and closely related fields through university nomination.

Explore the program

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Keywords

Computer Science
Machine Learning
Electrical Engineering
Information Technology
Deep Learning
Mathematics
Computer Vision
Real-time Systems
Robotics
Statistics
Autonomous System

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

University Name
Country flag

Denmark

Research Interests

Statistics

Artificial Intelligence

Electrical Engineering

Computer Vision

Computer Science

Professor at Aarhus University

University Name
Country flag

Denmark

Research Interests

Internet Of Things

Cloud Computing

Computer Science

Electrical Engineering

Deep Learning