Postdoc in Mathematical Modeling of Fungal Plant Disease at University of Göttingen, Germany
The University of Göttingen in Germany is inviting applications for a postdoctoral research position in mathematical modeling of fungal plant disease dynamics. The position is based in the Division of Plant Diseases and Crop Protection within the Department of Crop Sciences (DNPW). The successful candidate will work at the intersection of mathematical biology, computational modeling, plant pathology, epidemiology, and data-driven research. The position is offered at 100% employment under salary grade TV-L E13 and is initially limited to 2.5 years, with the possibility of an extension depending on funding availability. Application Deadline: September 11, 2026 Starting Date: February 1, 2027, or shortly thereafter
The University of Göttingen Postdoc in Mathematical Modeling of Fungal Plant Disease Dynamics is a strong opportunity for quantitative researchers interested in applying advanced mathematical and computational methods to important agricultural and biological problems. The position combines TV-L E13 salary at 100% employment, a 2.5-year initial contract, high-performance computing, extensive plant disease datasets, AI-supported image analysis, and close collaboration with experimental plant pathology researchers.
Candidates with expertise in mathematical biology, applied mathematics, physics, computational biology, or quantitative agricultural sciences should consider applying, particularly if they are interested in developing independent research at the intersection of disease epidemiology, mathematical modeling, and crop protection. Application deadline: September 11, 2026.
Postdoctoral Research Position at the University of Göttingen
The research position focuses on developing mathematical and computational models to understand the dynamics of foliar fungal diseases affecting crop plants. The research will connect disease processes occurring at the scale of individual leaves with the development and spread of epidemics across crop stands and agricultural fields. The successful researcher will have the opportunity to develop an independent research project while working closely with experimental plant pathology and epidemiology researchers. The biological system and modeling approach will be selected in consultation with the principal investigator based on the candidate’s expertise and research interests.
Key Details
| Position | Details |
|---|---|
| Position | Postdoc in Mathematical Modeling of Fungal Plant Disease Dynamics |
| Institution | University of Göttingen |
| Department | Department of Crop Sciences (DNPW) |
| Division | Plant Diseases and Crop Protection |
| Position Number | 76623 |
| Salary | TV-L E13 / 100% |
| Contract | Fixed-term, initially 2.5 years |
| Possible Extension | Subject to funding availability |
| Start Date | February 1, 2027, or shortly thereafter |
| Location | Göttingen, Germany |
| Application Deadline | September 11, 2026 |
| Contact | Alexey Mikaberidze |
Research Focus
The research group investigates how foliar fungal diseases develop and spread across multiple biological and spatial scales. A major objective is to connect disease processes at the leaf level with epidemic dynamics at the field level. At the individual leaf scale, researchers may investigate processes such as:
- Infection
- Latency
- Lesion expansion
- Sporulation
- Pathogen fitness
At the crop and field scale, the research examines how these processes contribute to the spread and development of disease epidemics.
Fungal Pathogens Under Investigation
The research group works with important fungal diseases of crop plants, particularly wheat diseases. Potential pathosystems include:
- Septoria tritici blotch caused by Zymoseptoria tritici
- Yellow or stripe rust caused by Puccinia striiformis
- Brown or leaf rust caused by Puccinia triticina
The precise biological focus of the successful applicant’s project will depend on their expertise and research interests.
Major Scientific Questions
The broader research programme examines fungal plant disease dynamics across the three components of the disease triangle:
- Pathogen genotype
- Host genotype
- Environment
One important research challenge is understanding how disease processes at the leaf scale translate into epidemic development at the field scale. Another major question concerns pathogen evolution. Researchers investigate how pathogens adapt to resistant crop varieties and fungicides, how quickly control measures can lose effectiveness, and which deployment strategies may help slow pathogen adaptation.
Potential Postdoctoral Research Projects
The successful candidate will develop and lead a distinct research project with focused biological and mathematical questions. Potential research directions could include:
Scaling Leaf-Level Fitness to Epidemic Dynamics
One possible research direction is to determine how fitness traits measured at the individual leaf scale influence epidemic velocity at larger spatial scales.
Eco-Evolutionary Dynamics of Resistance Breakdown
Another potential area is investigating how fungal pathogens adapt to disease-resistant crop varieties and how resistance breakdown can be understood using mathematical and evolutionary models. The exact project will be developed in discussion with the principal investigator.
A Unique Combination of Modeling and Experimental Research
A major strength of this research group is the close integration of mathematical modeling with experimental plant pathology and epidemiology. The group has already collected more than 50,000 high-resolution RGB images of diseased wheat leaves. These datasets include images associated with septoria tritici blotch, yellow rust, and brown rust. The existing dataset has already contributed to more than ten scientific publications. The research programme is also expanding into additional optical sensing technologies, including:
- Hyperspectral imaging
- Multispectral imaging
- Thermal infrared sensing
- LiDAR
This provides the successful postdoctoral researcher with an unusually rich empirical foundation for developing, parameterizing, and validating mathematical models.
Computational Research Opportunities
The position is particularly suited to researchers with strong quantitative and computational backgrounds. The successful candidate will work with large datasets and mathematical models to investigate disease dynamics and estimate biological parameters. The research may involve:
- Mathematical modeling
- Numerical simulations
- Statistical inference
- Model fitting
- Parameter estimation
- Uncertainty quantification
- High-performance computing
- Image-derived phenotyping data
The group also has support from a dedicated engineer working on AI-based image analysis and optical sensing.
Required Academic Background
Applicants must hold a PhD in theoretical or mathematical biology, applied mathematics, physics, or another related quantitative field. Candidates who are close to completing their PhD may also be considered. Alternatively, applicants with a PhD in biological or agricultural sciences may be eligible if they have demonstrated a strong research record in quantitative and mathematical modeling.
Essential Modeling Skills
Candidates should have an excellent command of dynamical systems modeling, particularly:
- Ordinary differential equations
- Stochastic processes
Experience with additional mathematical approaches is highly desirable. These include:
- Partial differential equations
- Integro-differential equations
- Large-scale computational modeling
- Spatio-temporal models
- Population dynamics
- Eco-evolutionary modeling
Experience with Eulerian or Lagrangian modeling of spore dispersal would also be an advantage.
Statistical and Data Analysis Experience
Experience with statistical inference and fitting mathematical models to experimental data is considered a strong asset. Knowledge of Bayesian methods and related approaches to parameter estimation would be particularly valuable. The successful candidate should be comfortable connecting mathematical models with real experimental datasets.
Programming Requirements
Strong scientific programming skills are desirable. The preferred programming language is Python, although experience with comparable languages such as:
- C
- C++
- R
may also be relevant. Candidates should understand the importance of reproducible computational workflows and maintain careful computational practices throughout the research process.
Plant Pathology Experience
Previous experience in plant pathology or plant biology is considered an advantage, but it is not a mandatory requirement. The biological background will be supported by the principal investigator and experimental research team. What matters most is a genuine interest in plant diseases and the ability to approach biological questions quantitatively. The successful candidate should be able to translate an important biological question into a mathematical framework and use analytical or numerical methods to investigate it.
Main Responsibilities
The postdoctoral researcher will be responsible for several major research activities.
Develop Biological Research Questions
The researcher will formulate clear and scientifically interesting questions or hypotheses concerning fungal disease dynamics. These questions will provide the foundation for mathematical modeling and computational analysis.
Develop and Parameterize Models
The researcher will develop deterministic and stochastic mathematical models using appropriate approaches such as:
- ODEs
- PDEs
- Integro-differential equations
- Spore dispersal models
- Other relevant computational frameworks
Models will be implemented, solved, and parameterized using experimental data generated within the research group or information available in the scientific literature.
Conduct Computational Analysis
The researcher will analyze models analytically and numerically. This may include fitting models to image-derived disease phenotyping data, estimating model parameters, and quantifying uncertainty.
Publish Research
The successful candidate will prepare manuscripts for international peer-reviewed journals. They will also present research findings at scientific conferences and meetings.
Support Future Research Funding
The position also provides opportunities to contribute to grant proposals for future research projects, fellowships, and other funding opportunities.
What the University of Göttingen Offers
The successful candidate will join an interdisciplinary research environment combining mathematical modeling with experimental plant pathology and epidemiology. Key benefits of the research environment include:
- Large experimental datasets
- More than 50,000 RGB disease images
- Emerging hyperspectral and multispectral datasets
- Thermal infrared and LiDAR data
- High-performance computing resources
- AI image analysis support
- International research collaborations
- Interdisciplinary scientific training
- Support for independent research development
- Mentoring for fellowship and grant applications
The researcher will also have opportunities to develop their own scientific ideas and shape future research directions. This position offers a particularly strong combination of mathematical modeling, computational science, experimental plant pathology, and agricultural research. It may be especially attractive to researchers interested in:
- Mathematical biology
- Computational biology
- Plant disease modeling
- Epidemiological modeling
- Eco-evolutionary dynamics
- Agricultural data science
- Scientific computing
- Crop protection
- Disease resistance
- AI-assisted plant phenotyping
The availability of large experimental datasets and high-performance computing resources provides a strong foundation for ambitious quantitative research.
Career Development and Research Independence
The group actively supports researchers who want to develop toward scientific independence. The successful postdoctoral researcher may receive mentoring for future funding applications, including opportunities related to DFG funding and group-leader fellowships. This makes the position particularly attractive for researchers who are preparing for a future independent academic career.
Working Environment
The University of Göttingen describes its working environment as diverse, international, and family-friendly. The university is committed to equality and supporting employees in balancing professional and family responsibilities. Qualified women are particularly encouraged to apply in fields where women are underrepresented. The university also welcomes applications from people with severe disabilities and provides support for professional participation.
English and German Language Requirements
An excellent command of written and spoken English is required. Knowledge of German is considered an advantage but is not required for the position. This makes the opportunity accessible to international researchers who have strong English-language academic and communication skills.
Required Application Documents
Applicants must submit one PDF file containing the required documents in the following order:
- Cover letter — maximum 2 pages
- Curriculum vitae
- Publication list
- Copy of PhD certificate
- Names and contact details of two referees
Applicants should carefully follow the requested document order when preparing the application.
How to Apply
Applications must be submitted through the University of Göttingen’s online application portal. Please upload your application in one pdf file including the usual documents until 9/11/2026 on the application portal of the university using this link: http://obp.uni-goettingen.de/de-de/OBF/Index/76623. For more information get in touch with Alexey Mikaberidze directly via E-Mail: alexey.mikaberidze@uni-goettingen.de, Tel. +495513923701 . Applicants should submit their complete application before: September 11, 2026 The position is scheduled to begin on: February 1, 2027, or shortly thereafter
Contact Information
For questions about the position, applicants can contact: Alexey Mikaberidze. Division of Plant Diseases and Crop Protection University of Göttingen. Email: alexey.mikaberidze@uni-goettingen.de Telephone: +49 551 3923701
Frequently Asked Questions
What is the position?
It is a postdoctoral research position in mathematical modeling of fungal plant disease dynamics at the University of Göttingen in Germany.
What is the salary?
The position is advertised at salary grade TV-L E13 with 100% employment.
The exact net salary will depend on the applicable German public-sector pay scale, tax situation, and other individual factors.
How long is the contract?
The position is initially offered as a 2.5-year fixed-term contract.
An extension may be possible depending on the availability of research funding.
When does the position start?
The planned starting date is February 1, 2027, or shortly thereafter.
What PhD background is required?
Applicants should have a PhD in theoretical or mathematical biology, applied mathematics, physics, or another related quantitative discipline. Candidates with biological or agricultural science PhDs may also be considered if they have strong quantitative and mathematical modeling experience.
Is plant pathology experience mandatory?
No. Plant pathology or plant biology experience is an advantage but is not required. The research group provides the necessary biological grounding, while strong quantitative skills and genuine interest in plant diseases are particularly important.
What mathematical skills are important?
Strong experience with dynamical systems, ordinary differential equations, and stochastic processes is expected. Experience with PDEs, integro-differential equations, spatio-temporal models, population dynamics, and eco-evolutionary modeling is highly desirable.
Is programming experience required?
Strong scientific programming skills are desirable, preferably in Python or a comparable language such as C, C++, or R. Experience with reproducible computational workflows is also valuable.
Is English required?
Yes. Applicants need an excellent command of written and spoken English.
Do I need to speak German?
No. German is considered an advantage but is not required.
What diseases will the research cover?
Potential pathosystems include septoria tritici blotch, yellow or stripe rust, and brown or leaf rust affecting wheat. The exact research focus will be determined according to the successful candidate’s expertise and interests.
What data will the postdoc work with?
The group has more than 50,000 high-resolution RGB images of diseased wheat leaves and is expanding its research to hyperspectral, multispectral, thermal infrared, and LiDAR sensing.
Is high-performance computing available?
Yes. The position includes access to high-performance computing resources for large-scale simulations and statistical inference.
What documents are required?
Applicants must submit one PDF containing a maximum two-page cover letter, CV, publication list, PhD certificate, and the names and contact details of two referees.
What is the application deadline?
The deadline is September 11, 2026.
Where is the position located?
The position is based at the University of Göttingen in Göttingen, Germany, within the Department of Crop Sciences and Division of Plant Diseases and Crop Protection.
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