PhD in Network Science: Modeling of Animal Networks, Zürich (Kreis 11)

PhD in Network Science: Modeling of Animal Networks 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.

This project uses 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. It investigates inbreeding avoidance in wild house mice using a novel multilayer temporal network modelling framework that integrates behavioural, genetic, and social co‑nesting data, andaims to develop new statistical and computational tools applicable 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.

This position is funded for four years and offers a competitive salary. Zurich provides a high quality of life and a vibrant scientific community.

Your Responsibilities

Develop and implement temporal multilayer network models combining behavioural, 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

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