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 [email protected].

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 [email protected]. 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 [email protected]. 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.