PhD Candidate in Explainable Machine Learning for Studying Biology at Single-Cell Resolution
Faculty of Science
Master's
38 hours
€3,204 - €4,051
Closes on 31-10-2026
Are you a MSc graduate with background in data science, computer science, biostatistics, bioinformatics or a related field? Do you have a solid foundation in machine learning? Are you passionate about biology and interested in building new machine learning and AI to accelerate biological discoveries? Then this position is for you!
Working at the UvA
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.
All about this vacancy
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.
Your place at the UvA
About the Faculty of Science
Researchers and students at the Faculty of Science are fascinated by every aspect of how the world works, whether it concerns elementary particles, the birth of the universe, or how the brain functions. The Faculty has a student population of approximately 8,000 students, as well as 1,800 staff members working in education, research, or support services.
Important to know
Your application & contact
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 apply button. We accept applications until and including 31 October 2026.
Do you have any questions, or do you require additional information? Please contact:
dr. ir. Stavros Makrodimitris,
prof. Dr. Aalt-Jan van Dijk.
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.
(for details: national knowledge security guidelines)
Acquisition in response to this vacancy is not appreciated.
Diversity, Equity & Inclusion
As an employer, the UvA maintains an equal opportunities policy. We value diversity and are fully committed to being a place where everyone feels at home. We nurture inquisitive minds and perseverance and allow room for persistent questioning. With us, curiosity and creativity are the prevailing culture.
Studies show that women and members of underrepresented groups only apply for jobs if they meet 100% of the qualifications. Do you meet the educational requirements but not yet all of the requested experience? The UvA encourages you to apply anyway.