Job offer

Are you interested in evolution, statistical physics, and infectious disease dynamics? Join an interdisciplinary PhD project that uses large-scale viral genome and epidemiological data to develop predictive theory for rapidly evolving viruses. You will work at the interface of theoretical physics, stochastic processes, statistical inference, and epidemiology, with SARS-CoV-2 and influenza as key case studies.

Your job In this project, you will develop a quantitative theory of evolution that can be tested directly against large-scale viral genomic data. The central challenge of this project is to connect microscopic evolutionary mechanisms-mutation, selection, and ecological feedback-to macroscopic, population-level observables in rapidly evolving pathogens such as SARS-CoV-2 and influenza.

Concretely, you will:

• formulate and analyze stochastic models of evolving populations using methods from statistical physics, applied probability, and population genetics;
• develop inference frameworks that link model predictions to genomic and epidemiological data;
• design controlled computational experiments (simulations and synthetic datasets) to validate theoretical predictions;
• apply your methods to large-scale viral datasets to test theoretical hypotheses and identify new regularities;
• contribute to an interdisciplinary research environment at the interface of physics, epidemiology, and complex systems.

This position offers the opportunity to work on a problem where theory, data, and societal relevance meet. The outcome of this PhD will not only sharpen our fundamental understanding of evolutionary dynamics but also inform intervention strategies to steer evolution toward less harmful outcomes.

You will be co-supervised across the Institute for Theoretical Physics (ITP) by Dr. Farshid Jafarpour and the University Medical Center Utrecht (UMCU) by Dr. Ganna Rozhnova, and embedded in the Centre for Complex Systems Studies (CCSS), where you will interact with researchers working on a broad range of complex systems.

We are looking for a motivated new colleague who meets the following criteria:

• you hold, or you will before September 2026, obtain a master's degree in theoretical physics, applied mathematics, computational biology, mathematical epidemiology, or a related quantitative field;
• you have strong training in at least one of the following fields: stochastic processes, statistical physics, statistical inference, population genetics, or closely related quantitative modeling;
• you have solid programming skills and experience with data analysis and reproducible computational workflows;
• you are interested in connecting quantitative theory to biological or epidemiological data;
• you can communicate clearly in English;
• you enjoy interdisciplinary collaboration and are willing to be embedded in the CCSS community.

Nice to have Experience with viral genomics, phylogenetics, Bayesian or likelihood-based inference, infectious disease modeling, or high-performance computing.

• a position for 18 months, with an extension to a total of four years upon a positive assessment in the first 18 months;
• a full-time working week and a gross monthly salary between € 3.059 and €3.881 in the case of full-time employment (salary scale P under the Collective Labour Agreement for Dutch Universities (CAO NU));
• 8% holiday pay and 8.3% year-end bonus;
• a pension scheme, partially paid parental leave and flexible terms of employment based on the CAO NU.

In addition to the terms of employment laid down in the CAO NU, Utrecht University also offers a range of its own schemes for employees. This includes arrangements for professional development, various types of leave, and options for sports and cultural activities. You can also tailor your employment conditions through our Terms of Employment Options Model. In this way, we encourage you to keep investing in your personal and professional development. For more information, please visit Working at Utrecht University. Selection process

As Utrecht University, we want to be a home for everyone. We value staff with diverse backgrounds, perspectives and identities, including cultural, religious or ethnic background, gender, sexual orientation, disability or age. We strive to create a safe and inclusive environment in which everyone can flourish and contribute.

Knowledge security screening can be part of the selection procedures of academic staff. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology.