项目介绍
The PhD researcher will be part of the eMedia research lab under the supervision of Prof. Bart Vanrumste. The research group is embedded in the Department of Electrical Engineering (ESAT) of KU Leuven, Europe’s most innovative university [Reuters]. Prof. Vanrumste’s research focuses on multimodal sensor integration and machine learning for monitoring of older persons and patients with chronic diseases.
Project
The position is embedded in JustHealth, an interdisciplinary EU‑funded doctoral network uniting experts in user‑centred design, medicine, ethics, governance, biomedical signal processing, and machine learning. Together, the consortium will tackle the key bottlenecks that still prevent AI tools from reaching daily clinical practice—namely structural bias, limited stakeholder trust, unclear routes to regulatory compliance, and technical robustness. Your work will focus on biomedical signal processing and machine learning, specifically in the subfield of theumatic heart disease detection.
Profile
We are looking for a dynamic and motivated PhD candidate with a strong interest in AI, who is interested in studying how advancements in machine learning can lead to improved monitoring of rheumatic heart disease. The candidate will be responsible for research and development of advanced AI-pipelines and will be involved in the JustHealth project. The candidate will also contribute to teaching activities related to machine learning or other areas depending on the candidate’s profile. Moreover, the candidate is a team player that enjoys collaborating with people within the research group, the project, and beyond, and has:
- A master’s degree in Engineering with a background in mechanical engineering, electrical engineering, computer science, AI, or related field, from a reputable institute, with outstanding study results;
- Programming experience in Python, particular experience in common deep learning frameworks (e.g., PyTorch and TensorFlow) would be a benefit;
- The qualities to carry out independent research, demonstrated e.g., by the grades obtained in your (under)graduate program(s);
- An excellent command of the English language, both in spoken and written form;
- Is comfortable assisting in data collection experiments with participants in general but older ones in particular;
- Willing to carry out brief research stays at research institutes abroad;
- A critical mindset.
Offer
We can offer the PhD candidate:
- A doctoral scholarship of four years and, if successful, a PhD in Engineering Technology,
- A competitive salary and additional benefits such as health insurance, access to university sports facilities, etc.
- The opportunity to be active in an exciting and international research environment, engage in research collaborations and participate at international conferences,
- A full-time employment for four years, with an intermediate evaluation after each year,
- An excellent doctoral training at the Arenberg Doctoral School in an international environment at a top European university,
- A flexible working culture with opportunity of up to 40% remote working.
Interested?
For more information please contact Prof. dr. ir. Bart Vanrumste, tel.: +32 16 32 64 07, mail: bart.vanrumste@kuleuven.be.You can apply for this job no later than August 29, 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.
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联系方式
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