Aarhus University
1 week ago
Postdoctoral Position in Adaptive and Distributed Foundation Model Inference Across the Computing Continuum Aarhus University in Denmark
Degree Level
Postdoc
Field of study
Computer Science
Funding
This is a full-time, fixed-term, two-year postdoctoral appointment, initially from March 1, 2027 to February 28, 2029, with possible extension. Salary and employment terms follow the collective labor agreement between the Danish Ministry of Taxation and the relevant professional associations, with salary at basic salary steps 4–8. The position offers access to computing infrastructure, research-career support, mentoring, international networking, and employee services, but no specific stipend am
Deadline
Dec 6, 2026
Country
Denmark
University
Aarhus University
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About this position
Aarhus University in Denmark invites applications for a full-time, fixed-term postdoctoral position in adaptive and distributed foundation model inference across the computing continuum. The postdoctoral researcher will join the Department of Electrical and Computer Engineering and work on resource-efficient deployment of large foundation models across edge and cloud computing environments.
Research topics include distributed and collaborative inference strategies, Mixture-of-Experts placement and scheduling, token and intermediate-feature compression, and resource-aware, communication-efficient inference optimization. The research environment supports publications in leading venues and collaboration with academic and industry partners in a Horizon Europe project.
Applicants must hold a PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related discipline. Candidates should have expertise in deep learning, Transformers, foundation models, Python, PyTorch or related frameworks, distributed systems, and edge AI, together with a strong publication record and excellent English communication skills. Experience with stream data and cross-cultural collaborative research is advantageous.
The position is supervised by Professor Qi Zhang, whose research includes edge intelligence, distributed foundation model inference, Internet of Things, and analytics on compressed data. The post is based at Aarhus University's Department of Electrical and Computer Engineering in Aarhus N, Denmark, and runs for two years from March 1, 2027, or as soon as possible thereafter, until February 28, 2029, with possible extension. Applications must be submitted by December 6, 2026, at 23:59 CET. The application should include a CV, degree certificate, publication list, research plan, research-activity information, and teaching materials where applicable.
Funding details
This is a full-time, fixed-term, two-year postdoctoral appointment, initially from March 1, 2027 to February 28, 2029, with possible extension. Salary and employment terms follow the collective labor agreement between the Danish Ministry of Taxation and the relevant professional associations, with salary at basic salary steps 4–8. The position offers access to computing infrastructure, research-career support, mentoring, international networking, and employee services, but no specific stipend am
What's required
Applicants must hold a PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field. Required qualifications include a strong background in deep learning, including Transformers and foundation models; strong Python programming skills and experience with deep learning frameworks such as PyTorch; experience with distributed systems and edge AI; a strong publication record in reputable conferences or journals relative to career stage; and excellent written and spoken English communication skills. Experience with stream data and collaborative, cross-cultural research environments is advantageous.
How to apply
Submit the application in English through Aarhus University's recruitment system using the direct application link. Include a CV, degree certificate, complete publication list, future research plan, information on research activities, teaching portfolio, and verified information on previous teaching experience if applicable.
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Prepare your application
Review drafts and confirm requirements with the university.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.