Aarhus University PhD fellowship/scholarship

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.