PhD Position in Machine Learning for Prenatal Care

01/07/2025

The project

Early detection of fetal abnormalities and complications is key to be prepared and improve the final outcome. To do so, each pregnancy undergoes several scans, including ultrasound and other biomarkers (like some molecules or proteins). However, this can be costly, as while basic ultrasound scans can be done fast, a comprehensive examination can take an hour. Therefore, it is crucial to reduce costs (and stress for the mother) andavoid unnecessary acquisitions, that will not result in a clinical benefit. This is special critical in low-and-middle income countries, where they do not have
the resources to scan all pregnancies many times, and due to the larger rate of fetal adverse outcomes, they need to concentrate on the pregnancies that are at risk.

The aim of this PhD project is to improve ante-natal care by developping reliable and interpretable ML methods to provide guidance in the amount and order of clinical information used to reach an adequate decision at the individual level. You will develop sequential and hierarhcical ML models to guide data collection and identify from limited information, which pregnancies are healthy, and for those higher suspicion of abnormalities which is the most adequate examination (and when) to follow- up for the suspected pathology.

This position is funded by a PIF1/PRC fellowship offered by the Department of Engineering at Universitat Pompeu Fabra, which requires a teaching assistance load of up to 45 hours per academic year.

Preferred qualifications

- Bachelor and Msc in Biomedical Engineering, Mathematics, Physics or related disciplines.
- Skills on machine learning and (medical) image processing, as well as handling time-series data.
- [Bonus] Previous experience in reinforcement learning, causal machine learning and/or model interpretability and explainability
- Programming skills in Python.
- Previous knowledge of medicine is not required, but interest in learning the clinical aspects of the project and interacting with clinicians.
- Curiosity, interest in open research questions.

Why join Us?

• Worldclass environment – Barcelona Center for New Medical Technologies (Department of Engineering, Universitat Pompeu Fabra)  offers an ideal working environment, with a large critical mass of experienced senior investigators in diverse areas of biomedical engineering, junior postdoctoral researchers, and an international team of talented young PhD students.
• Work towards solving clinical relevant problems, with AI experts and in ultrasound technologies, in close collaboration with clinical experts in fetal medicine from several institutions, in Barcelona (BCNatal; Hospital Sant Joan de Deu and Maternitat de Barcelona) and abroad.
• Be supervised by Prof. Bart Bijnens (ICREA research professor, specialised in computarised analysis of ultrasound images) and by Dr. Gabriel Bernardino (Ramon y Cajal fellow, specialised in machine learning for understanding cardiovascular physiology).
• Vibrant Barcelona life – a cosmopolitan city, Englishfriendly graduate school, and travel budget for conferences and stays.

What we offer

- Fouryear fully funded contract (salary + social security)
- Salary according to the UPF PIF1/PRC scale, which is currently: 

1470.98 € gross / month during the 1st and 2nd years
1576.05 € gross / month in the 3rd year
1970.06 € gross / month in the 4th year

- Full tuition fees, equipment and conference travel budget.
- Additional funding opportunities. The group will actively support applications to competitive fellowships and participation in projectbased topups to further increase remuneration.
- Access to UPF’s doctoral training programme and transferable skills courses.

How to apply

Email a single PDF to [email protected] and [email protected] with:

- CV (2 pages max)
- Transcripts (BSc + MSc)
- [Optional] Cover letter (1 page: why you, why us)
- Contact details of two referees

Shortlisted candidates will be invited to a remote technical interview and to present a short research proposal.

Application deadline: 31/07/2025