Stellenangebot: Postdoc in mathematical modeling of fungal plant disease dynamics (all genders welcome) bei Georg-August-Universität Zentr. Universitätsverwaltung

An der Georg-August-Universität Göttingen Stiftung Öffentlichen Rechts – DNPW - Abt. Pflanzenkrankheiten und Pflanzenschutz ist ab dem 01.02.2027 eine Stelle alsPostdoc in mathematical modeling of fungal plant disease dynamics (all genders welcome)- Entgeltgruppe 13 TV-L - in Vollzeit (teilzeitgeeignet) zu besetzen. Die Stelle ist a 2.5 years fixed-term contract.“Postdoc in mathematical modeling of fungal plant disease dynamics” at the Division of Plant Diseases and Crop Protection. Salary grade 13 TV-L / 100%. The position is initially limited to 2.5 years, with the possibility of extension subject to availability of funding. Starting date is Februay 1st, 2027, or shortly thereafter. Deadline for applications: September 11th, 2026.
The position focuses on mathematical and computational modeling of the dynamics of foliar fungal pathogens of crop plants, across scales spanning disease development on individual leaves and epidemic progress in crop stands and fields. The modeling framework and biological focus will be chosen in discussion with the PI, depending on the successful applicant's expertise and interests, and may draw on the pathosystems studied in our group: septoria tritici blotch (Zymoseptoria tritici), yellow (stripe) rust (Puccinia striiformis) and brown (leaf) rust (Puccinia triticina).
Our broader research program aims to understand and predict foliar fungal disease dynamics across scales and across the three components of the disease triangle: pathogen genotype, host genotype and environment. A central challenge is to link the leaf scale, at which disease develops through infection, latency, lesion expansion and sporulation, to the field scale, at which epidemics spread through crop stands over a season. A second central question is evolutionary: how does the pathogen adapt to disease-resistant cultivars and to fungicides, how fast is their efficacy eroded, and what deployment strategies can slow this adaptation? Within this framework, the successful candidate will frame and lead a distinct project with a focused set of research questions — for example on the mechanistic scaling from leaf-level fitness traits to epidemic velocity, or on the eco-evolutionary dynamics of resistance breakdown. Familiarity with plant pathology or biology is considered an asset but is not a requirement; the biological grounding is provided by the PI and the experimental team, and what matters most is a genuine interest in plant diseases and the ability to engage with them quantitatively.
A distinctive strength of our group is that it combines mathematical modeling of plant disease dynamics with experimental plant pathology and epidemiology at a high level. We have already gathered 50,000 high-resolution RGB images of diseased wheat leaves (septoria tritici blotch, yellow rust and brown rust) and this dataset has already formed the basis of more than 10 publications. We continue to collect such data — including time-resolved series — and are now expanding into further optical sensing techniques (hyperspectral, multispectral, thermal infrared and LiDAR). 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 University of Göttingen, with its Department of Crop Sciences (DNPW), is one of Germany's leading centers for agricultural and crop sciences. Göttingen is a historic, green and international university town with fast ICE rail links to major cities and an excellent quality of life.

Your profile
We are looking for a highly motivated postdoctoral researcher with strong quantitative and computational skills and a genuine interest in plant diseases. Applicants must hold a Ph.D. in theoretical or mathematical biology, applied mathematics, physics, or a related quantitative field, or be on the verge of completing their Ph.D.; alternatively, a PhD in biological or agricultural sciences with a proven track record of strong quantitative and mathematical modeling research. The candidate should have an excellent command of dynamical systems modeling with ordinary differential equations and stochastic processes. Experience with partial and integro-differential equations, and large-scale computation on high-performance computing facilities is desirable. Experience with spatio-temporal models (incl. Eulerian/Lagrangian modeling of spore dispersal), population dynamics and eco-evolutionary modeling, as well as with statistical inference and model fitting to data (e.g. Bayesian methods), will be considered strong assets. Solid scientific programming skills, preferably in Python (or a comparable language such as C/C++ or R), including reproducible workflows, are desirable. The ability to learn new methods in modeling and computation in the context of plant disease dynamics is essential. Above all, the successful candidate should be able to pose clear and interesting biological questions in mathematical terms, answer them through analytical or numerical methods and communicate the outcomes to an interdisciplinary audience.
The position requires strong organizational skills, careful and reproducible computational practice, and the ability to work independently while contributing to an interdisciplinary research environment. An excellent command of written and spoken English is required; knowledge of German is a plus, but not required.

Your tasks
-Formulate clear and interesting biological questions / hypotheses on fungal disease dynamics to guide the modeling research.
-Model development and parameterization: based on these questions, formulate mathematical models using deterministic and stochastic approaches (e.g. ODEs, PDEs, integro-differential equations), and, where relevant, Eulerian/Lagrangian modeling of spore dispersal; implement and solve them, and parameterize them using data generated in the group or from the broader literature.
-Computation and analysis: analyze models both analytically and numerically, and fit them to the group's image-derived phenotyping data, estimate model parameters and quantify uncertainty to answer the biological questions posed above.
-Scientific writing and communication: prepare manuscripts for international peer-reviewed journals; present at scientific meetings; and write or contribute to grant proposals for follow-up projects or fellowships.

What we offer
-A unique research environment that combines mathematical modeling with experimental plant pathology and epidemiology.
-An integration with an experimental team and a large, growing dataset ( 50,000 RGB images plus upcoming hyperspectral, multispectral, thermal IR and LiDAR data), giving the modeling research a solid empirical foundation.
-Support from a dedicated engineer for AI image analysis and optical sensing.
-Access to high-performance computing resources for large-scale simulation and inference.
-Active support in developing toward research independence, including mentoring for own grant and fellowship applications (e.g. DFG, group-leader fellowships).
-Integration into a strong collaborative research network within Germany, across Europe and worldwide.
-A growing lab with opportunities to shape research directions and develop independent ideas.
-A family-friendly, diverse and international working environment in which we value diversity and equality.

Please upload your application in one pdf file with the documents in the following order: (1) cover letter (max 2 pages), (2) CV, (3) publication list, (4) copy of PhD certificate, (5) names and contact details of two referees.Die Universität Göttingen strebt in den Bereichen, in denen Frauen unterrepräsentiert sind, eine Erhöhung des Frauenanteils an und fordert daher qualifizierte Frauen nachdrücklich zur Bewerbung auf. Sie versteht sich zudem als familienfreundliche Hochschule und fördert die Vereinbarkeit von Wissenschaft/Beruf und Familie. Der beruflichen Teilhabe von schwerbehinderten Beschäftigten sieht sich die Universität in besondere Weise verpflichtet und begrüßt deshalb Bewerbungen schwerbehinderter Menschen. Bei gleicher Qualifikation erhalten Bewerbungen von Menschen mit Schwerbehinderung den Vorzug. Eine Behinderung bzw. Gleichstellung ist zur Wahrung der Interessen bereits in die Bewerbung aufzunehmen.Bitte reichen Sie Ihre aussagekräftige Bewerbung mit allen wichtigen Unterlagen bis zum 11.09.2026 ausschließlich über das Bewerbungsportal http://obp.uni-goettingen.de/de-de/OBF/Index/76623 ein. Auskunft erteilt Herr Alexey Mikaberidze, E-Mail: alexey.mikaberidze@uni-goettingen.de, Tel. +495513923701Hinweis: Wir weisen darauf hin, dass die Einreichung der Bewerbung eine datenschutzrechtliche Einwilligung in die Verarbeitung Ihrer Bewerbungsdaten durch uns darstellt. Näheres zur Rechtsgrundlage und Datenverwendung finden Sie im Hinweisblatt zur Datenschutzgrundverordnung (DSGVO)