Relevant doctoral degree (required)
Completed PhD in theoretical or mathematical biology, applied mathematics, physics or a related quantitative field; alternatively, a PhD in the biological or agricultural sciences with a proven track record of quantitative and mathematical modeling research.

Mathematical and computational modeling of dynamical systems (required)
Demonstrated command of dynamical systems modeling with ordinary differential equations and stochastic processes, together with the ability to frame biological questions in mathematical terms and answer them through analytical and numerical methods.

Proficiency in English (required)
Excellent command of written and spoken English, sufficient to publish, present and collaborate in an international research environment. Knowledge of German is a plus, but not required.

Interest in plant disease biology (desirable)
Genuine interest in plant diseases and a motivation to engage with plant pathology and epidemiology quantitatively; prior familiarity with plant pathology or biology is an asset, though the essential biological grounding is provided by the PI and the experimental team.

Spatio-temporal and eco-evolutionary modeling (desirable)
Experience with partial and integro-differential equations, spatio-temporal models (including Eulerian/Lagrangian modeling of spore dispersal), population dynamics and eco-evolutionary modeling, as well as large-scale computation on high-performance computing facilities.

Statistical inference and model fitting (desirable)
Experience with statistical inference and fitting models to data, for example Bayesian methods, to parameterize and validate models and to quantify uncertainty.

Scientific programming and reproducible workflows (desirable)
Solid scientific programming skills, preferably in Python (or a comparable language such as C/C++ or R), including reproducible computational workflows.

Publication track record (desirable)
Track record of publishing high-quality research in international peer-reviewed journals, preferably in a relevant field.