Publisher
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Aarhus University

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 flag

Country

Denmark

University

Aarhus University

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Keywords

Computer Science
Electrical Engineering
Information Technology
Deep Learning
Internet Of Things
Distributed System
ML

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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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