Behzad Bozorgtabar

A fully funded PhD position is available at Aarhus University, Denmark, in the Department of Electrical and Computer Engineering, focusing on Test-Time Adaptation and Agentic AI for Multimodal Foundation Models. The position is part of the A3 Lab – Adaptive & Agentic AI, and aims to develop robust machine learning systems capable of adapting at test time under real-world distribution shifts. Research will address the challenges faced by modern foundation models, such as vision–language and multimodal models, which often degrade after deployment due to changes in data, environment, sensors, or user behavior. The project will design methods for safe and efficient adaptation of these models post-deployment, emphasizing reliability and efficiency. Key research directions include test-time adaptation, agentic decision-making mechanisms, adaptive systems for reliability monitoring, distribution shift detection, trustworthy sample selection, and lightweight model updates. The project also explores feedback-driven and reward-based adaptation, uncertainty estimation, calibration, and out-of-distribution detection. The candidate will contribute to novel algorithms, theoretical insights, and large-scale empirical evaluations, with a strong focus on reproducibility and real-world impact. The successful candidate will join a dynamic research environment, collaborate internationally, and have opportunities to publish at leading venues such as NeurIPS, ICML, ICLR, CVPR, and ECCV. Open-source contributions and interdisciplinary collaborations are encouraged. Applicants must have a relevant Master’s degree in Electrical Engineering, Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a closely related field, with strong machine learning and/or computer vision background, solid Python programming skills, and experience with deep learning frameworks like PyTorch. Prior research experience is advantageous. The position is fully funded, with salary and terms according to the collective agreement. The application deadline is 26 February 2026, and the preferred start date is 15 May 2026. For further information, contact Associate Professor Behzad Bozorgtabar at [email protected]. To apply, submit a statement of interest, CV, academic transcripts, diplomas, and the project description as a PDF via the application link. Aarhus University values equality and diversity and encourages all qualified candidates to apply.

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.

A fully funded PhD position is available at the Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The successful candidate will join the newly established A3 Lab – Adaptive & Agentic AI, located in the Department of Electrical and Computer Engineering. The research project focuses on developing robust and reliable machine learning systems capable of adapting at test time under real-world distribution shifts. Modern foundation models, including vision–language and multimodal models, often perform well during training but may degrade after deployment due to changes in data, environment, sensors, or user behaviour. This PhD aims to design methods that enable such models to safely adapt after deployment while maintaining reliability and efficiency. Key research directions include test-time adaptation for multimodal foundation models and agentic decision-making mechanisms that determine when, how, and whether adaptation should occur. The project involves developing adaptive systems that monitor their own reliability, detect distribution shifts, select trustworthy samples, and apply lightweight updates or fallback strategies as needed. Additional areas of exploration include feedback-driven and reward-based adaptation, uncertainty estimation, calibration, and out-of-distribution detection. The candidate will work on novel algorithms, theoretical insights, and large-scale empirical evaluations, with a strong emphasis on reproducibility and real-world relevance. The position offers opportunities for international collaboration and publication at leading machine learning and computer vision venues such as NeurIPS, ICML, ICLR, CVPR, and ECCV. The research environment at Aarhus University is dynamic and encourages contributions to open-source software and interdisciplinary collaborations. The place of employment is Aarhus University, and the place of work is the Department of Electrical and Computer Engineering, Faculty of Technical Sciences, Finlandsgade 22, 8200 Aarhus N, Denmark. Applicants must hold a relevant master’s degree (120 ECTS) in Electrical Engineering, Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a closely related field. Those close to completing their master’s degree may also be considered if the degree is completed before enrollment. A strong background in machine learning and/or computer vision is required, along with solid programming skills in Python and experience with deep learning frameworks such as PyTorch. Prior research experience, including a master’s thesis, publications, or substantial research projects, is considered an advantage. To apply, submit a 1-page statement of interest describing your background, research interests, and fit for the project, a CV (including publication list, if any), and academic transcripts and diplomas. Applications must be submitted via the provided link, and a PDF copy of the project description should be uploaded. Only documents received before the application deadline will be evaluated. The programme committee may request further information or invite applicants to attend an interview. Shortlisting will be used to evaluate the most relevant applications. Aarhus University values equality and diversity and encourages all interested candidates to apply, regardless of personal background. Salary and terms of employment are in accordance with the applicable collective agreement. The application deadline is 01 June 2026, and the preferred starting date is 01 August 2026 or later. For further information regarding the PhD position, contact Associate Professor Behzad Bozorgtabar at [email protected].