Academic ConnectPhD position on Hybrid Methods for Robust Acoustic Sensing in Automotive Applications
_(ref. BAP-2025-341)_
_Last modified: 10/06/25_
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.
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 focuses on hybrid methods for robust acoustic sensing.
In automotive applications such as autonomous driving, traffic surveillance, and vehicle health monitoring, there is growing interest in acoustic sensing to leverage the rich information encoded in audible sound emissions. Relevant use cases include localizing vehicles, estimating their speed, detecting emergency sirens, monitoring road surfaces, and enabling predictive maintenance within the vehicle. However, publicly available data is often limited and unlabelled, posing challenges for training machine learning models that generalize across domains. By comparison, classical DSP approaches are often grounded in physical modeling rather than relying on data, but struggle under real-world conditions due to noise or oversimplifications. This PhD project aims to address these challenges in selected automotive use cases, in collaboration with industrial and academic partners. The research will focus on the development of hybrid solutions that integrate DSP and machine learning, collecting and synthesizing training data, and applying cross-domain learning techniques.
The project will be embedded in the international research group EAVISE. The standard duration for PhD research at KU Leuven is four years.
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. Applicants are invited to include their Master’s thesis or a link to it, if available.
- 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 hybrid methods for robust acoustic sensing in automotive applications, 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?
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 July 16, 2025 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.
Do you have a question about the online application system? Please consult our FAQ or email us at apply@kuleuven.be
av_timer Appointment percentage: Full-time
location_city Location: Sint-Katelijne-Waver
timer Apply until:
16/07/2025 23:59 CET
bookmarks Tags: Engineering Technology
