Postdoc – multi-modal explainable AI for early detection and risk stratification in familial hypercholesterolemia

About your role

With your research you will contribute to the development of novel risk assessment and patient stratification tools for familial hypercholesterolemia (FH). This includes identifying new factors that drive disease progression. The primary focus will be on using state-of-the-art deep learning models for high-dimensional data, alongside other machine learning techniques for other types of data, to build predictive models. For each data modality, you will design an appropriate (deep) machine learning architecture to extract predictive features. In the next stage, you will develop and apply post hoc explainability methods, including techniques such as activation mapping and "this looks like that" approaches, to investigate which input features contribute most to model predictions. You will work with existing FH patient cohort data sets from France, Portugal, Czech Republic, and Turkey, which will be expanded with multi-level multi-omics data, including genetic, transcriptomic, metabolomic, and cellular data.

Your core research tasks include:
- Co-developing and applying (explainable) AI techniques (particularly deep learning).
- Writing scientific articles for publication in leading journals.
- Presenting your research findings at international conferences.

In addition to your core research tasks, you will prepare progress reports, deliverable documents, and present your project updates during internal consortium meetings and external review meetings. You will also represent your work package within the consortium, not only in formal meetings but through ongoing communication with partners across disciplines and institutions. Actively ensuring alignment and integration of your work package with the broader project goals will be essential to its success.  

About your workplace

This position is available within the FH-EARLY project titled "New strategies for the early diagnosis, risk stratification and co-management of familial hypercholesterolemia". This FH-EARLY project is funded within the scope of the Horizon Europe funding instrument HORIZON Research and Innovation Actions (HORIZON-RIA) under the call: "Comparative effectiveness research for healthcare interventions in areas of high public health need - Tackling diseases and reducing disease burden" (HORIZON-HLTH-2024-DISEASE-03-08-two-stage). With a consortium of 15 internationally leading institutions, this Horizon Europe project of 48 months, started in January 2025 aiming to enable new strategies for earlier diagnosis and co-management of familial hypercholesterolemia (FH).

At LUMC, you will join the AI-based Innovations research group within the department of radiation oncology. Your work will be part of a project that contributes to our research focus on explainable AI. Thereby you will become part of the national Innovation Center for AI (ICAI) lab on explainable AI for health. Within this project you are expected to work closely together with another postdoc who will be appointed at the national research institute for mathematics and computer science in the Netherlands (in Dutch: Centrum Wiskunde & Informatica (CWI).

About you

You have a genuine interest in both fundamental and applied research. You are flexible, motivated, and enjoy collaborating with colleagues from a variety of disciplines. You communicate clearly and are well-organized in your work. Your curiosity and enthusiasm make you feel at home in a multidisciplinary environment, and you actively seek connection—for example, within a large European consortium and in close collaboration with colleagues at CWI.

In addition, you have:

• a PhD (or are in the final stages of completing one) in computer science, data science, bioinformatics, computational biology, or a related field.
• proven experience with programming languages and tools commonly used in bioinformatics or data science.
• hands-on experience in analyzing high-dimensional data.
• excellent command of English, both written and spoken.

Our offer

Driven by health; that's our mission. This applies not only to our patients but also to our employees. We strive to provide a safe, inclusive and equitable work environment, with a focus on talent development and connected leadership. In order to be able to continue to learn and develop, we offer internal and external training. You are also entitled to an end-of-year bonus (8,3%), holiday allowance, sports budget, bicycle scheme, home office allowance and an excellent commuting allowance. Furthermore, as an employee of LUMC, you are also affiliated with the ABP pension fund. This means that 70% of your pension premium is paid by LUMC, leaving you with a higher net salary. Nice, right?

See all employment conditions of the LUMC.

What do we represent?

As an academic medical center, LUMC strives to have a workforce that is a good reflection of society. We are committed to the highest quality in health care, education and (international) research, in which diverse perspectives are essential. Sustainability is also a high priority for us: we are committed to a healthy future, not only for people, but also for the planet. In line with these values, we aim to be a creative and inspiring place to work, where everyone feels at home, safe and valued. It's all about who you are and what you bring to contribute, regardless of your background. Together, we work toward a sustainable and inclusive future, both for the people around us and for the world we live in. Together, we are LUMC.  

More information

• As a postdoc, your salary will be between €3,598 and €5,669 gross per month (scale 10, CAO UMC). These figures are based on a full-time position.
• The procedure involves several stages, starting with a first interview to be scheduled in the first two weeks of August with Tanja Alderliesten (LUMC) and Peter A.N. Bosman (CWI) for which you will be asked to give a presentation.
• Applications must include:
  • A cover letter, including personal motivation and qualifications for the position (maximum two A4).
  • A detailed curriculum vitae, including the contact information of three references.
  • Lists of courses followed (at M.Sc. level) including obtained grades.
• The starting date is January 1^st
• Applications from employment/recruitment agencies will not be considered.