This PhD position at KU Leuven, within the EAVISE research group, focuses on developing domain-aware audio representations for low-resource settings. EAVISE is a multidisciplinary group operating at the intersection of artificial intelligence, embedded systems, computer vision, and sound processing, and is part of the Electrical Engineering Department (ESAT) and Computer Science Department at KU Leuven. The project aims to advance audio representation learning by designing model structures and learning objectives that explicitly incorporate domain knowledge, particularly for scenarios where training data is limited and compact models are necessary.

Application areas include predictive maintenance, process monitoring, environmental monitoring, and safety-critical event detection. The research will address challenges in data efficiency, computational efficiency, and domain generalization by disentangling sound representations based on signal characteristics.

The successful candidate will join an international research environment with access to state-of-the-art infrastructure and benefit from close mentorship, participation in courses, workshops, and conferences, and opportunities for collaboration with academic and industrial partners. The position offers a competitive salary or tax-free PhD grant, with an initial one-year appointment and potential extension up to four years.

Candidates are expected to conduct research, contribute to project planning, disseminate findings, assist in supervising Master’s students, and participate in limited teaching activities. Applicants must have a Master’s degree in electrical or computer engineering (or a related field), strong mathematical background, coursework in signal and sound processing, proficiency in Python, and excellent English communication skills. Research experience in sound processing and additional programming skills are advantageous. KU Leuven is committed to diversity and inclusion, providing a supportive and respectful environment for all researchers.

The application deadline is December 3, 2025, and applications must be submitted via the online tool.