Postdoc in mathematical modeling of fungal plant disease dynamics

Start: 1 February 2027 or shortly after.
Application deadline: 11 September 2026
Location: Goettingen, Germany
How to apply: one PDF (cover letter, CV, publication list, PhD certificate, two referees) via the university portal.
Official advert & application: https://www.uni-goettingen.de/de/644546.html?details=76623
Contact: alexey.mikaberidze@uni-goettingen.de

A 2.5-year postdoctoral position (E13 TV-L, 100%, extension possible subject to funding) is available in the group of Alexey Mikaberidze (Plant Diseases and Crop Protection, University of Goettingen).

Using mathematical and computational modeling, the postdoc will describe and predict how foliar fungal diseases of wheat develop across scales — from infection, latency, lesion expansion and sporulation on individual leaves to epidemic spread through crop stands and fields over a season. Possible research questions are centered around the ecology and evolution of plant-pathogen interactions: how leaf-scale fitness traits scale up to epidemic velocity, and how pathogens adapt to resistant cultivars and fungicides — how fast their efficacy erodes, and which deployment strategies can slow this adaptation. Modeling approaches span deterministic and stochastic dynamical systems (ODEs, PDEs, integro-differential equations) and spatial models of spore dispersal. Main pathosystems can be Zymoseptoria tritici (septoria tritici blotch), Puccinia striiformis (yellow/stripe rust) or Puccinia triticina (brown/leaf rust). The successful candidate will frame and lead a distinct project within this framework.

A distinctive strength of our group is that it combines mathematical modeling of disease dynamics with experimental plant pathology and epidemiology. Our large and growing imaging dataset (>50,000 high-resolution RGB images of diseased wheat leaves, now expanding to hyperspectral, multispectral, thermal IR and LiDAR) has already underpinned >10 peer-reviewed publications. This growing wealth of data offers exceptional opportunities to parameterize and validate the models against disease dynamics, in collaboration with experimental researchers within the group. The Department of Crop Sciences (DNPW) is one of Germany's leading centers for agricultural and crop sciences, and Goettingen is a historic, green and international university town with fast ICE rail links and an excellent quality of life.

We seek a motivated researcher holding (or close to completing) a PhD in theoretical or mathematical biology, applied mathematics, physics or a related quantitative field — or in the biological or agricultural sciences with a strong quantitative and mathematical modeling track record. We expect an excellent command of dynamical-systems modeling (ordinary differential equations and stochastic processes) and solid scientific programming (preferably Python, or C/C++ or R). A genuine interest in plant diseases is essential; the biological grounding is provided by the PI and the experimental team. Fluent English required; German not necessary.

Your tasks:
- Formulate clear and interesting biological questions and hypotheses on fungal disease dynamics to guide the modeling research.
- Model development and parameterization: build deterministic and stochastic models (ODEs, PDEs, integro-differential equations) and, where relevant, Eulerian/Lagrangian models of spore dispersal; implement, solve and parameterize them using group data or the literature.
- Computation and analysis: analyze models analytically and numerically, fit them to the group's image-derived phenotyping data, estimate parameters and quantify uncertainty.
- Scientific writing and communication: publish in international peer-reviewed journals, present at conferences, and contribute to grant proposals.

What we offer:
- A supportive research environment combining mathematical modeling with experimental plant pathology and epidemiology.
- Integration with an experimental team and a large, growing dataset (>50,000 RGB images plus upcoming hyperspectral, multispectral, thermal IR and LiDAR), and support from a dedicated AI-imaging engineer.
- Access to high-performance computing for large-scale simulation and inference.
- Support toward research independence, including mentoring for your own grant and fellowship applications (e.g. DFG, group leader fellowships).
- A collaborative network across Germany and worldwide, and room to shape research directions in a growing lab.
- A family-friendly, diverse and international working environment

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