项目介绍

2026年4月19日

The research group EAVISE is a multidisciplinary research group, based on Campus De Nayer Sint-Katelijne-Waver of KU Leuven, and belongs to the research divisions PSI (Processing of Speech and Images) of the Electrical Engineering Department (ESAT) and DTAI (Declarative Languages and Artificial Intelligence) of the Computer Science Department. The group conducts research in demand-driven applications of state-of-the-art algorithms for artificial intelligence, embedded systems, computer vision, and sound processing in industry-specific applications. EAVISE can build upon a solid research infrastructure, an extensive international network, connections with companies and non-profit organizations, and a supportive work environment.

Website van de eenheid

Project

A PhD research position is available at KU Leuven, Department of Electrical Engineering (ESAT), division Processing Speech and Images (PSI), within the research group EAVISE. The project explores audio representation learning for low-resource settings.

Recent advances in machine learning for audio have focused on learning representations from raw data, typically using large-scale models that incorporate only minimal prior domain knowledge. While such representations can support a wide range of downstream tasks, they are often data‑hungry and computationally demanding. In many application areas however, such as predictive maintenance, process monitoring, and environmental monitoring, training data is limited, models must generalize across conditions, and deployment constraints require compact and efficient architectures. This PhD project explores how to adapt self‑supervised audio representation learning to these low‑resource settings. Specifically, we aim to design suitable model structures and learning objectives that disentangle sound representations by explicitly incorporating prior knowledge about acoustic signal structure reflecting the underlying sound generation process. By making this acoustic structure explicit in model design and training, we aim to improve data efficiency, computational efficiency, robustness, and domain generalization of learned audio representations.

The project will be embedded in the international research group EAVISE. The standard duration for PhD research at KU Leuven is four years.

Starting date: as soon as practical, preferably by September 2026.

Profile

We are looking for a highly motivated PhD candidate who meets the following profile:

• Candidates must hold a Master’s degree in electrical or computer engineering (or a closely related field), with a solid foundation in mathematics (e.g., matrix algebra) and coursework in areas such as digital signal processing, sound processing, and machine learning.
• Research experience (e.g., a Master’s thesis or research internship) in sound processing is highly valued. We particularly encourage applications from candidates whose thesis work received above-average grades, awards, or led to scientific publications. 
• Proficiency in Python is required. Experience with additional programming languages, such as MATLAB or C/C++, is considered a plus.
• Excellent English language proficiency is required, along with strong oral and written communication skills.

As part of the research position, the selected candidate will be expected to: 

• Conduct research on audio representation learning for low-resource settings, and contribute to project planning by monitoring milestones and ensuring timely delivery of results.
• Actively participate in internal research and project meetings, and collaborate with academic or industrial partners where applicable.
• Disseminate research findings through academic publications and conference presentations.
• Contribute to the development of follow-up research proposals.
• Assist in the supervision of Master’s thesis students.
• Perform a limited amount of teaching activities (maximum two hours per week).
• Enroll in a doctoral training program at the Arenberg Doctoral School and fulfill the school’s coursework requirements for PhD researchers.

Offer

The successful candidate will benefit from:

• A PhD degree from one of Europe’s top universities (after approximately four years of successful research).
• A comprehensive scientific education within the framework of a structured doctoral training program and close mentorship by experienced researchers.
• A high-level, stimulating, and international research environment with access to state-of-the-art research infrastructure.
• Opportunities to participate in local and international courses, workshops, and conferences.
• A competitive salary or tax-free PhD grant (depending on the funding scheme).
• An initial one-year appointment, with the possibility of extension for a second, third, and fourth year.
• Flexible working hours and the possibility of remote work, where appropriate.

Interested?

Please submit your application by May 12, 2026 via the online application tool (link below). Applications sent by email will not be considered. Your application should include:

• A cover letter describing your motivation for applying to this position.
• Your CV, including your GPA, details of relevant coursework, a link to your Master’s thesis (if available), and a list of publications (if applicable). If you have not yet obtained your Master’s degree, please indicate the expected graduation date.
• Transcripts of your Master’s degree (or current Master’s studies, if still in progress), as well as any other relevant records (e.g., language certificates).

For more information please contact Prof. dr. Thomas Dietzen via email at thomas.dietzen@kuleuven.be.You can apply for this job no later than May 12, 2026 via the online application tool

KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address.

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