项目介绍

2026年5月1日

We are seeking a highly motivated PhD student to join our Quantitative Network Science research group at the Department of Mathematical Modeling and Machine Learning at the University of Zurich.

Wild populations are increasingly exposed to demographic decline and fragmentation, making inbreeding and its consequences a central problem in evolutionary biology and conservation. We address this using one of the most comprehensive datasets available for any vertebrate population: a long-term field study in Switzerland of more than 20,000 wild mice that vary naturally in degree of inbreeding. This project investigates inbreeding avoidance in wild house mice using a novel multilayer temporal network modeling framework that integrates behavioral, genetic, and social co-nesting data, and aims to develop new statistical and computational tools applicable well beyond the study system. The PhD project is embedded in a collaborative framework with network scientists and evolutionary biologists at the University of Zurich and at Columbia University.

You will be embedded in an active and collaborative research group with access to a unique long-term dataset and outstanding scientific support. You will benefit from close collaboration with leading groups in biology, network science and genomics. We are committed to fostering an inclusive and respectful working environment.

The position is funded for 4 years and offers a competitive salary. Zurich offers a very high quality of life and a vibrant scientific community.

Your responsibilities

• Develop and implement temporal multilayer network models combining behavioral, genetic, and social data
• Develop and implement network null models for statistical tests of inbreeding avoidance
• Build simulations of mouse population dynamics for benchmarking and power analysis
• Collaborate closely with biologists and geneticists to ensure the biological validity of computational methods
• Disseminate results in high-quality peer-reviewed journals and at international conferences

Your profile

• Fascinated by fundamental questions in network science and complex systems, with an interest in evolutionary biology
• MSc (or equivalent) in applied mathematics, physics, computational biology, or a closely related field
• Strong programming skills (Python); familiarity with graph libraries is an advantage
• Background in network science, graph theory, stochastic processes, or statistical inference
• Ability to work independently in an interdisciplinary research environment
• Strong interpersonal and team collaboration skills
• Fluency in spoken and written English

Information on your application

To learn more about the research conducted by the Quantitative Network Science group, visit our webpage.

For any inquiries about the position, please get in touch with Prof. Alexandre Bovet at phd_animalnetworks@proton.me.

To apply, please upload the following documents in English to our job portal:

• A motivation letter explaining your interest in the position, how you meet the criteria, how you envision contributing to the research project, and how it aligns with your career goals.
• A CV
• Degree certificates and grade transcripts
• Arrange for two referees who are willing to prepare a recommendation letter which will have to be sent by June 3rd, 2026
• An example of a significant research contribution you have made (e.g., a Master’s thesis or if not completed, a summary of it), along with a 300–500-word summary outlining why you consider this contribution significant and describing your role in the research.

The deadline for applications is May 27th, 2026, CEST.