Qi Zhang

This postdoctoral position at Aarhus University focuses on Edge AI for Goal-Oriented Semantic Communication, with a particular emphasis on developing semantic-aware communication systems for 6G networks. The successful candidate will join the Department of Electrical and Computer Engineering, working within the Communication, Control & Automation Section under the supervision of Professor Qi Zhang. The research will involve semantic representation and reasoning on time-series data, designing and implementing Edge AI solutions for real-time analytics and decision-making, and creating energy-efficient, low-latency communication strategies. The position is part of the Nordic University Collaboration on Edge Intelligence (NUEI) project, funded by NordForsk, and offers opportunities to participate in seminars and co-supervise bachelor’s and master’s thesis projects. Applicants should have a PhD in computer engineering, electrical engineering, communication engineering, computer science, or a related field, with documented experience in deep learning (CNNs, Transformers), strong Python programming skills, and familiarity with deep learning frameworks such as PyTorch. Experience with time-series data, Internet of Things, and wireless communication networks is essential, along with a strong publication record and excellent English communication skills. Additional experience with explainable AI and collaborative research environments is advantageous. Aarhus University provides a vibrant, inclusive research environment with access to state-of-the-art facilities, strong support for career development, and a commitment to diversity, equity, and work-life balance. Denmark offers excellent quality of life, family-friendly policies, and public services. The position is full-time, fixed-term for one year starting February 1st, 2026, with the possibility of a one-year extension. Salary and employment conditions are in accordance with Danish university agreements. Applications must be submitted in English via the university’s recruitment system by November 24th, 2025, and should include all required documentation. For further information, applicants may contact Professor Qi Zhang at qz@ece.au.dk.

The Department of Electrical and Computer Engineering at Aarhus University is inviting applications for a postdoctoral position focused on efficient and distributed foundation model inference across the computing continuum, from cloud to edge. This research opportunity is ideal for candidates interested in scalable AI systems, edge intelligence, and communication-efficient artificial intelligence. The successful applicant will join a dynamic research group led by Professor Qi Zhang, whose expertise includes edge intelligence, semantic communications, and analytics on compressed data. The postdoctoral researcher will contribute to cutting-edge research topics such as token compression, adaptive token pruning, distributed and collaborative inference strategies, Mixture-of-Experts (MoE) architectures, resource-aware and latency-constrained inference optimization, and on-device deployment of foundation models. The position is part of the Horizon Europe Project and offers opportunities for collaboration with leading academic and industry partners, as well as publication in top-tier venues. Applicants should have a Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a related field, with a strong background in deep learning (including Transformers and foundation models), programming skills in Python, experience with deep learning frameworks like PyTorch, and familiarity with distributed systems and edge AI. A strong publication record and excellent English communication skills are required. Experience with stream data, goal-oriented communications, and collaborative, cross-cultural research environments is advantageous. The position is a full-time, fixed-term appointment for one year, starting September 1, 2026, with the possibility of a 1–2 year extension based on performance. Salary and employment conditions are determined by Danish collective agreements, and the university offers a vibrant, inclusive research environment with state-of-the-art facilities, strong support for career development, and a commitment to diversity, equity, and work-life balance. Denmark is renowned for its high quality of life, family-friendly policies, and excellent public services. To apply, candidates must submit their application via Aarhus University’s recruitment system, including a CV, degree certificate, publication list, research plan, and teaching portfolio. Reference letters should be arranged in advance. For further information, contact Professor Qi Zhang at qz@ece.au.dk. The application deadline is April 30, 2026. For more details, visit the official job posting: Aarhus University Postdoc Position .

Join the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on efficient foundation model inference across the computing continuum, from Cloud to Edge. This one-year, full-time, fixed-term postdoc (with possible extension) offers the opportunity to contribute to cutting-edge research in communication-efficient and distributed foundation model inference. The research aims to enable scalable, low-latency, and resource-aware deployment of large foundation models, with topics including token compression, adaptive token pruning, distributed and collaborative inference strategies, Mixture-of-Experts architectures, resource-aware and latency-constrained optimization, and edge intelligence for on-device deployment. As a postdoc, you will work in a dynamic and collaborative environment, publishing in top-tier venues and collaborating with leading academic and industry partners within the Horizon Europe Project. The position is supervised by Professor Qi Zhang, whose research focuses on Edge Intelligence, Goal-oriented Semantic Communications, Internet of Things, and analytics on compressed data. Aarhus University is committed to excellence in research, education, and innovation, with research spanning IoT, machine learning, signal processing, and digital twins, emphasizing high-impact and societal relevance. Denmark offers an outstanding work-life balance, family-friendly policies, subsidised childcare, public healthcare, and excellent education. Aarhus University provides a supportive workplace culture, state-of-the-art facilities, strong support for research career development, mentoring, international networking, and a commitment to diversity, equity, and inclusion. The place of work is Helsingforsgade 10, 8200 Aarhus N, and the area of employment is Aarhus University with related departments. Applicants must have a Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a related field, with a strong background in deep learning (e.g., Transformers, foundation models), programming skills in Python and experience with deep learning frameworks (e.g., PyTorch), experience with distributed systems and edge AI, a strong publication record, and excellent English communication skills. Advantageous qualifications include experience with stream data, Goal-oriented Communications, and collaborative, cross-cultural research environments. Applications must be submitted in English and include a CV, degree certificate, publication list, statement of future research plans, information about research activities, teaching portfolio, and verified teaching experience. The application deadline is April 30, 2026. Shortlisting is used, and all applicants will be notified about the assessment process. Letters of reference can be uploaded by referees, but must be arranged in advance. Salary is determined by seniority as agreed between the Danish Ministry of Taxation and the Confederation of Professional Associations. Aarhus University offers relocation services and career counselling for international researchers and accompanying families. For further information, contact Professor Qi Zhang at qz@ece.au.dk. Apply via Aarhus University's recruitment system under the job advertisement. For additional details, visit the application link provided.

PhD opportunity at Aarhus University in Agentic Test-Time Adaptation for Efficient and Reliable Edge Intelligence , hosted by the Department of Electrical and Computer Engineering within the Graduate School of Technical Sciences. The project sits at the intersection of computer science , electrical engineering , machine learning , computer vision , foundation models , edge intelligence , and autonomous AI . The successful candidate will join the newly established A3 Lab – Adaptive & Agentic AI , directed by Behzad Bozorgtabar (main supervisor) and co-supervised by Qi Zhang . The research focuses on building low-latency, high-reliability test-time adaptation methods for unimodal and multimodal foundation models operating in dynamic edge environments. Research themes include autonomous monitoring of distribution shifts, uncertainty estimation, on-the-fly adaptation under strict computational constraints, and balancing adaptation accuracy with energy efficiency and real-time execution. The post highlights applications in mission-critical settings such as autonomous robotics and industrial monitoring, and mentions opportunities to publish in venues such as NeurIPS, ICML, and CVPR. Funding: The position is fully funded as a PhD fellowship/scholarship, with salary and employment terms according to the applicable collective agreement. Eligibility: Applicants should 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, a solid ML/CV background, and familiarity with Transformers, advanced CNNs, knowledge distillation, lightweight architectures, or parameter-efficient fine-tuning are preferred. Interest in Test-Time Adaptation, Continual Learning, Machine Unlearning, and multimodal foundation models is especially relevant. Application: Deadline is 20 May 2026 at 23:59 CEST . Applicants must include a 1-page statement of interest, CV, and academic records. A project description must also be uploaded as a PDF by copying the provided project text. Apply through the official link before the deadline; only complete applications received on time will be considered.

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 behzad@ece.au.dk or qz@ece.au.dk.

Aarhus University is advertising a Postdoctoral Researcher position in efficient and distributed foundation model inference across the edge-cloud computing continuum . The role sits in the Department of Electrical and Computer Engineering and is linked to research on scalable AI systems, communication-efficient inference, edge intelligence, and resource-aware deployment of large foundation models. The successful candidate will work on topics such as token compression , adaptive token pruning , distributed and collaborative inference , Mixture-of-Experts architectures , latency-constrained optimization , and on-device foundation model deployment . The postdoc will join a collaborative environment with opportunities to publish in top venues and engage with academic and industry partners in Europe through a Horizon Europe project. Eligibility highlights: applicants should hold a PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field. Strong experience in deep learning, transformers/foundation models, Python, PyTorch, distributed systems, and edge AI is expected. A strong publication record and excellent English communication skills are required. Experience with stream data, goal-oriented communications, and cross-cultural research collaboration is considered an advantage. Funding and appointment: this is a full-time, fixed-term postdoctoral appointment for one year starting 1 November 2026, with a possible extension of 1–2 years. Salary is according to Danish collective agreements and depends on seniority. No specific stipend amount is stated. Application window: the deadline is 30 August 2026 . Applications must be submitted in English through Aarhus University’s recruitment system and should include a CV, degree certificate, publication list, research plan, and relevant teaching documentation. Supervisor/contact: Professor Qi Zhang is listed as the supervisor and contact person.

Aarhus University in Denmark is recruiting a Postdoctoral Researcher for a project on Efficient Foundation Model Inference across the computing continuum. The research area includes Large Language Models (LLMs) , Foundation Models , Edge AI , Distributed AI , Cloud-to-Edge Computing , Mixture-of-Experts (MoE) , and resource-efficient AI systems . The post is based in the Department of Electrical and Computer Engineering and is described as a full-time fixed-term appointment. The selected researcher will work under Professor Qi Zhang in an internationally collaborative environment, including Horizon Europe projects. The role emphasizes token compression, adaptive token pruning, distributed and collaborative inference, latency-constrained optimization, and scalable deployment of foundation models on edge and cloud systems. Eligibility highlights: a PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field; strong deep learning and transformer background; Python and PyTorch skills; experience with distributed systems and Edge AI; strong publication record; and excellent English communication skills. Preferred experience includes stream data, goal-oriented communications, and international research collaboration. Funding: the position is fully funded. The appointment is for 1 year, with a possible extension of 1–2 additional years. Deadline: 30 August 2026 (23:59 CEST). Applicants should apply online via the Aarhus University vacancy portal and upload a CV, degree certificates, publication list, research statement, and teaching portfolio if applicable.

Aarhus University is offering a 1-year postdoctoral position, with the possibility of a 1–2 year extension, in efficient foundation model inference across the computing continuum . The position is based at the Department of Electrical and Computer Engineering and is centered on communication-efficient and distributed inference from cloud to edge, with a strong focus on scalable, low-latency, resource-aware deployment of large foundation models. The successful candidate will contribute to research topics such as token compression and adaptive token pruning, distributed and collaborative inference strategies, mixture-of-experts architectures for scalable inference, resource-aware and latency-constrained optimization, and edge intelligence/on-device foundation model deployment. The work is embedded in a dynamic and collaborative environment with opportunities to publish in top-tier venues and engage with academic and industry partners within a Horizon Europe project. The ideal applicant should hold a Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a related field. A strong background in deep learning is required, particularly in transformers and foundation models, together with strong Python programming skills and experience with frameworks such as PyTorch. Experience with distributed systems and edge AI is also expected, along with an excellent publication record relative to career stage and strong English communication skills. Experience with stream data, goal-oriented communications, and collaborative cross-cultural research is considered an advantage. The position is supervised by Professor Qi Zhang , whose research interests include edge intelligence, goal-oriented semantic communications, Internet of Things, and analytics on compressed data. The postdoc will join a research environment at Aarhus University that emphasizes high-impact research, diversity, and strong support for career development. The appointment is full-time and fixed-term, starting on September 1, 2026 or as soon as possible thereafter, with an initial end date of August 31, 2027 (or 12 months from the actual start date). An extension of 1–2 additional years may be offered subject to satisfactory performance. The application deadline is 2026-04-30 . Applicants must submit materials in English through Aarhus University’s recruitment system. This opportunity is especially relevant for researchers in deep learning systems, distributed AI, edge intelligence, and efficient inference for large-scale foundation models.