ESSENTIAL REQUIREMENTS: A master's degree in Engineering and Engineering Technology with a background in electrical engineering, biomedical engineering, computer science, AI, or related field, from a reputable institute, with outstanding study results.
DESIRABLE REQUIREMENTS:
1. Programming experience in Python, particular experience in common deep learning frameworks (e.g., PyTorch and TensorFlow) ;
2. The qualities to carry out independent research, demonstrated e.g., by the grades obtained in your (under)graduate program(s);
3. Is comfortable assisting in data collection experiments with participants in school or community based data collection campaigns;
4. Willing to carry out brief research stays at research institutes abroad;
5. A critical mindset.
SELECTION CRITERIA
All eligible applications will be assessed by a Selection panel according to the following criterion “Qualifications and previous experience” and related sub-criteria:
Selection criterion and sub-criteria for admission to the shortlist
Qualifications and previous experience (0-50 )
A. quality of academic performance (12.5)
B. current knowledge and expertise(12.5)
C. relevant research skills (12.5)
D. motivation to apply(12.5)
Selection criterion and sub-criteria of shortlisted candidates
Communication and Personal skills (0-70)
E. Ability to design and conduct original research in the subject area of the individual research project (15)
F. Excellent oral communication in English(11)
G. Enthusiasm, proactivity, creativity and commitment (11)
H. Interpersonal skills(11)
I. Attitude to team working and in working in local and international setting(11)
J. Expected impact of the doctorate on the DC’s future career(11)
OBJECTIVES
Overall objective:
6. Develop, test, validate, and valorise a decolonized, just and trustworthy FMs and an FMs-based application for RHD staging for community-based screening (RHD screening and staging will be the 2nd use case to test and validate the Framework).
Specific research objectives:
7. Analyse technical gaps and obstacles that hinder the implementation of social justice in AI systems in healthcare for RHD; Identify the technical requirements for the development of just FMs-based solutions for RHD.
8. Develop a data collection workflow utilizing handheld echocardiography and Phonocardiography (PCG) for RHD screening and staging; Curate a comprehensive database of large-scale echocardiogram and heart sound data; Develop scalable FMs allowing clinicians to semi-automatically detect and mitigate biases in FM-based RHD screening and staging.
9. Develop an application for FM-based RHD screening and staging based on handheld echocardiography and PCG data; test and validate the FMs-based application for RHD screening and staging through a pilot action in an every-day life use setting.
10. According to the technical perspective, explore the possibility of adapting the FM-based application for RHD for use in diagnosing other related cardiovascular diseases; provide insights for standardisation from the technical/engineering perspective.
EXPECTED RESULTS
11. Curated and labelled dataset of handheld echocardiogram and PCG for the development of the proposed FM;
12. A FM for RHD screening and staging;
13. A FMs-based application for RHD screening and staging easily usable by a nurse or community health worker in a school or community screening programme.
INDICATIVE PLANNED SECONDMENTS - Institution, place and timing
14. University of Cape Town (Cape Town, South Africa); Nov. – Dec. 2026; Jun. – Aug. 2028
15. Emory University (Atlanta, USA); Mar. – Jun 2029
MAIN SUPERVISOR: Prof. Bart Vanrumste (https://www.kuleuven.be/wieiswie/en/person/00045098 )(e-mail: bart.vanrumste@kuleuven.be )
Due to the high transdisciplinarity of the project, the main supervisor will collaborate with other co-supervisors from other project’s partners.
PHD ENROLLMENT: The doctoral candidate will register with KU Leuven’s Arenberg Doctoral School (Science, Engineering & Technology group) and follow the Doctoral Programme in Engineering Technology delivered by the Faculty of Engineering Technology (FET). Progress is tracked in the KU Loket “roadmap” with formal milestone reviews at month 9, 21 and 36, after which the thesis may proceed to internal and public defence (target month 48). FET’s house rules underline industry‑oriented research, multi‑campus facilities and strong valorisation support, while the doctoral school offers complementary skills workshops, inter‑university course exchanges,providing a structured yet flexible framework for high‑impact doctoral work. DOCTORAL SCHOOL AND RESEARCH TEAM 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. Prof. Vanrumste’s research focuses on multimodal sensor integration and machine learning for monitoring of older persons and patients with chronic diseases
Duration of the employment: 48 months since 1st September 2026 (expected date of the recruitment)
Income: 4.010,00 € Gross per month (48.120,00€ / year).Benefits
710 € Mobility Allowance per month (8.520€ / year)
660 € Family Allowance per month (7.920€ / year) - Applicable only when the recruited doctoral candidate has family obligations according to the Marie Skłodowska-Curie rules, i.e., when the recruited Doctoral candidate has persons linked to him/her by:
16. marriage, or
17. a relationship with equivalent status to a marriage recognised by the legislation of the country or region where this relationship was formalised; or
18. dependent children who are actually being maintained by the doctoral candidate
Benefits are gross EU contribution to the salary cost of the doctoral candidate. The net salary will result from deducting all compulsory (employer/employee) national social security contributions as well as direct taxes.
HOW TO APPLYApplications must be sent exclusively in English and through the online application system cross-referenced in this vacancy. Applicationssent through other means or in other languages (other than English) will not be evaluated.Candidates are required to submit the following documents:1. a complete CV in Europass Format in English that must highlight activities and place where the activities have been carriedout in order to give evidence of fulfilling the mobility eligibility criterion (see above). Use the template available at https://europass.cedefop.europa.eu/it/documents/curriculum-vitae/templates- instructions;2. a complete academic CV in English with references to past research and training experiences;3. a motivation letter, in English, highlighting the consistency between the candidate‘s profile and the chosen DC position for whichhe/she is applying;4. at least 2 letters of Academic reference, in English or in certified translation;5. scan of the degree qualification, with certified translation in English (if the degree qualification is not in English);6. proof of language proficiency;7. scanned copy of valid identification document (identity card or passport);8. Declaration of Honour according to the template available on the website https://justhealth-project.eu/ for download;9. (OPTIONAL) Any further and relevant supporting documents (e.g., research publications, document attesting proficiency in anotherlanguage).