At the University of Göttingen -Public Law Foundation-, DNPW - Abt. Pflanzenkrankheiten und Pflanzenschutz, there is a position as
Postdoc in controlled-environment experiments with fungal pathogens of wheat (all genders welcome)
Entgeltgruppe 13 TV-L/100%
to be filled. Starting date is 2/1/2027. The position is limited to 31.07.2029.
“Postdoc in controlled-environment experiments with fungal pathogens of wheat” 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 February 1st, 2027, or shortly thereafter.
Deadline for applications: September 11th, 2026.
The position focuses on controlled-environment experimentation with major foliar fungal pathogens of wheat, with an emphasis on optical sensing and image-based phenotyping. The main pathosystem will be chosen in discussion with the PI, depending on the expertise and research interests of the successful applicant. Possible pathosystems include septoria tritici blotch (
Zymoseptoria tritici ), yellow (stripe) rust (
Puccinia striiformis ) and brown (leaf) rust (
Puccinia triticina ).
Our broader research program aims to characterize leaf-scale pathogen fitness traits across the three components of the disease triangle: pathogen genotype, host genotype and environment. Key traits measurable by RGB imaging include infection efficiency, incubation and latent period distributions, lesion expansion and sporulation; further traits can be defined using hyperspectral and thermal IR imaging. Within this framework, the successful candidate will frame and lead a distinct project with a focused set of research questions — for example on variation in pathogen fitness traits and trade-offs or synergies among them; on components of quantitative resistance in wheat that suppress these traits, using mapping populations; or on how temperature and humidity influence these traits. The outcomes are expected to inform predictive modeling of foliar wheat diseases and breeding for quantitative disease resistance. The successful candidate will work within an interdisciplinary team that provides technical, image-analysis and modeling support, and is not expected to master every one of these areas alone.
A distinctive strength of our group is that it combines experimental plant pathology and epidemiology with mathematical modeling of plant disease dynamics at a high level. Our large and growing imaging dataset (>50,000 high-resolution RGB images of diseased wheat leaves) has already formed the basis of more than 10 publications. The phenotyping data generated in this project will be published by the postdoc in their own right and, as an added benefit, feed into our modeling team to parameterize predictive models of disease dynamics — offering synergy between experiment and theory.
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 expertise in plant pathology, controlled-environment experimentation and quantitative disease phenotyping. Applicants must hold a Ph.D. in plant pathology, plant sciences, fungal biology, genetics, or a related field, or be on the verge of completing their Ph.D., and must have experience in experimental research on plant diseases, preferably in the area of fungal leaf pathogens in cereals. Experience with wheat diseases, controlled-environment inoculation experiments, disease phenotyping, optical sensing or digital image analysis will be considered a strong asset. Experience with genomic analyses of plant–pathogen interactions is a plus. Experience with reproducible data analysis (preferably in Python), including preprocessing, quality control, statistical analysis and scientific visualization, is desirable. The ability to learn new skills in experimentation and data analysis is essential.
The position requires strong organizational skills, careful experimental practice and the ability to work independently while contributing to an interdisciplinary environment. An excellent command of written and spoken English is required; knowledge of German is a plus, but not required.
Your tasks
- Controlled-environment experimentation: prepare wheat and pathogen material; conduct inoculation experiments; optimize conditions to compare pathogen isolates, wheat genotypes or environmental regimes.
- Optical sensing and image acquisition: collect time-resolved imaging data, using RGB imaging as the core approach and adding hyperspectral or thermal infrared sensing for selected questions; organize raw images and metadata reproducibly.
- Data analysis: apply AI image analysis pipelines to quantify disease development over time; analyze image-derived data, preferably in Python, including preprocessing, quality control, trait extraction, statistical analysis and data visualization.
- Genomic analysis of pathogen and/or host traits: where relevant, link phenotypic traits to genomic variation on the pathogen and/or host side, e.g. using GWAS, QTL mapping or genomic selection.
- Scientific writing and communication: prepare manuscripts for international peer-reviewed journals; present at meetings, seminars and conferences; and contribute to grant proposals for follow-up projects.
What we offer
- A unique research environment that combines experimental plant pathology and epidemiology with mathematical modeling.
- Support from experienced technical staff in controlled-environment experimentation and lab workflows with fungal wheat pathogens, and from a dedicated engineer for AI image analysis and optical sensing.
- Access to state-of-the-art optical sensing equipment, including RGB, multispectral, hyperspectral and thermal infrared imaging sensors.
- A strong modeling team, offering synergy between phenotyping experiments and predictive modeling of disease dynamics.
- Access to greenhouses and plant growth chambers for controlled-environment experiments with wheat pathogens.
- 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.
The University of Göttingen is an equal opportunities employer and places particular emphasis on fostering career opportunities for women. Qualified women are therefore strongly encouraged to apply in fields in which they are underrepresented. The university has committed itself to being a family-friendly institution and supports their employees in balancing work and family life. The University is particularly committed to the professional participation of severely disabled employees and therefore welcomes applications from severely disabled people. In the case of equal qualifications, applications from people with severe disabilities will be given preference. A disability or equality is to be included in the application in order to protect the interests of the applicant.
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/76559 . For more information get in touch with Alexey Mikaberidze directly via E-Mail:
[email protected], Tel. +495513923701 .
Please note:
With submission of your application, you accept the processing of your applicant data in terms of data-protection law. Further information on the legal basis and data usage is provided in the Information General Data Protection Regulation (GDPR)
