PhD candidate in Explainable Machine Learning for studying biology at single-cell resolution

Join Us!

We are looking for a motivated PhD candidate to develop novel methodology for the analysis of single-cell (multi)-omics data by incorporating existing biological knowledge into machine learning models. You will join a collaborative and internationally-oriented team working at the interface of computer science, mathematics, and biology in the Biosystems Data Analysis group at the Swammerdam Institute for Life Sciences at the University of Amsterdam.

We welcome applications from candidates with diverse backgrounds, experiences and perspectives. If you recognise yourself in the role but do not meet every listed preference, we encourage you to apply.

This is what you will do

Interpretable machine learning is a growing research area, with important applications in the biological sciences, such as understanding how different genes regulate each other within biological pathways. However, the current common practice is first to build complicated, “black-box” models and try to understand what they learned afterwards. This has the disadvantage that a) interpretation is still not always possible and b) it is inefficient as the model must re-discover patterns that are already well-known from scratch every time it is trained.

Recent technological advances have allowed us to study DNA and RNA not only in a “bulk” tissue, but also at a single-cell resolution, giving us unprecedented insights into how organisms form, how diseases develop, and how cells communicate with each other inside a tissue.

In this project, you will build interpretable-by-design machine learning models for biological data that offer biological insights in a direct way. Such models will be applicable to a variety of single-cell datasets and experiments in different fields of biology.

Tasks and responsibilities:

• complete a PhD thesis within the official appointment duration (four years);
• develop novel algorithms for analyzing single-cell (multi)-omics data and design and perform in silico experiments to test them;
• be an active and responsible member of the research group and collaborate closely with fellow group members;
• discuss work with group members and at departmental meetings, and incorporate feedback;
• take a leading role in writing manuscripts for publication in peer-reviewed journals and conferences;
• participate in the PhD training programme of the University of Amsterdam;
• assist with teaching and supervision of Bachelor’s and Master’s students.

You will have the opportunity to:

• work at the interface of machine learning and biology;
• collaborate closely with biologists from a variety of disciplines including immunology and cancer biology;
• present results at national and international scientific conferences;
• expand academic, professional and personal skills;
• use high-end compute infrastructure via the national ICT cooperative (SURF)

What we ask of you

You are passionate about research and want to develop into an independent scientist. You have a background in machine learning and artificial intelligence and like to build novel methods for the analysis of biological data. You are methodical, curious and able to take initiative, while also valuing close collaboration in an interdisciplinary and international research environment.

Your experience and profile

You

• hold a MSc in Bioinformatics, Artificial Intelligence, Data Science, Biostatistics or a closely related discipline;
• have demonstrable experience with training machine and deep learning models, preferably using Python;
• have a basic understanding of biology and/or are interested in using computational methods to understand biology;
• are fluent in English, written and spoken.

Experience with single-cell transcriptomics data analysis and working with high-performance computing systems are considered a plus.

This is what we offer you

A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). This should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and master students.

Your salary will range between €3,204 in the first year to €4,051 gross per month in the last year of employment, based on a fulltime employment (38 hours per week). This sum does not include the 8% holiday pay and the 8.3% end-of-year bonus. A favorable tax agreement, the 30% ruling, may apply to non-Dutch applicants. The Collective Labour Agreement for Dutch Universities (CAO NU) is applicable.

Besides the salary and a vibrant and challenging environment at Amsterdam Science Park we offer you multiple fringe benefits:

• 232 holiday hours per year (based on fulltime);
• multiple courses to follow from our Teaching and Learning Centre;
• a complete educational program for PhD students;
• a pension at ABP for which UvA pays two third part of the contribution;
• the possibility to follow courses to learn Dutch;
• help with housing for a studio or small apartment when you’re moving from abroad.

Are you curious to read more about our extensive package of secondary employment benefits, take a look here.

You will work in this team

You will be appointed in the Biosystems Data Analysis group (BDA), within the Cell and Systems Biology cluster of the Swammerdam Institute for Life Sciences (SILS).

The BDA group develops advanced methods to analyze complex biological and biomedical data. As advances in high-throughput -omics technologies produce vast datasets, our research focuses on developing algorithms and computational methods to detect patterns and generate insights. BDA blends a wide range of expertises, including bioinformatics, data mining, data fusion, machine learning, and systems modelling. We are at the forefront of method development towards large-scale data analysis and modeling of biological systems. Together with a wide range of collaborators, we contribute essential insights to biological questions spanning microbiology, plant science, neuroscience, and beyond.

The Swammerdam Institute for Life Sciences (SILS) is located at the vibrant Amsterdam Science Park. SILS is one of eight institutes of the University of Amsterdam's Faculty of Science (FNWI). With around 240 employees, SILS carries out internationally high-quality life science research and provides education within various university programs. Research is also carried out in close cooperation with the medical, biotech, chemical, flavor, food & agricultural, and high-tech industries, and revolves around 4 main themes, Cell & Systems biology, Neurosciences, Microbiology and Green Life Sciences.

The Faculty of Science has a student body of around 8,000, as well as 1,800 members of staff working in education, research or support services. Researchers and students at the Faculty of Science are fascinated by every aspect of how the world works, be it elementary particles, the birth of the universe or the functioning of the brain.

Want to know more about our organisation? Read more about working at the University of Amsterdam.

If you feel the profile fits you, and you are interested in the job, we look forward to receiving your application. You can apply online via the red apply button. We accept applications until and including 13 October 2026.

Do you have any questions, or do you require additional information? Please contact:

• dr. ir. Stavros Makrodimitris, s.makrodimitris@uva.nl
• prof. Dr. Aalt-Jan van Dijk, a.d.j.vandijk@uva.nl.

Applications should include the following information (all files besides your cv should be submitted in one single pdf file):

• a detailed CV including the months (not just years) when referring to your education and work experience;
• a letter of motivation;
• the names and email addresses of two references who can provide letters of recommendation.

A knowledge security check can be part of the selection procedure.