PhD Candidate in Explainable Machine Learning for Studying Biology at Single-Cell Resolution

University of Amsterdam; Netherlands; On-site; EUR 36,000 - 48,000; Full time

Job summary

University of Amsterdam seeks a motivated PhD candidate to develop novel methodology for single-cell (multi)-omics data by incorporating biological knowledge into machine learning models. You will join the Biosystems Data Analysis group at the Swammerdam Institute for Life Sciences.

You will work in an interdisciplinary team, contribute to publications, and participate in PhD training while collaborating with biologists and computer scientists.

Qualifications

• MSc in Bioinformatics, AI, Data Science, Biostatistics or related field.
• Experience training ML/DL models, preferably using Python.
• Interest in applying computational methods to biology; strong English communication.

Responsibilities

• Complete a PhD thesis within the official appointment duration (four years).
• Develop novel algorithms for analyzing single-cell (multi)-omics data and design tests.
• Collaborate with the research group and present at meetings; write manuscripts.
• Participate in the PhD training programme and assist with teaching/supervision.

Skills

Machine learning

Artificial intelligence

Python

Interdisciplinary

English fluency

Education

MSc in Bioinformatics

Biostatistics

Data Science

Tools

Python

HPC

Job description

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 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.

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.

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

If you feel the profile fits you, and you are interested in the job, we look forward to receiving your application. Applications should be included until and including 13 October 2026.

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

• dr. ir. Stavros Makrodimitris, [e-mail]
• prof. Dr. Aalt-Jan van Dijk, [e-mail].

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)