PhD Position in Grassland Science

At the University of Göttingen -Public Law Foundation-, DNPW - Abt. Graslandwissenschaft, there is a position as

to be filled. Starting date is 4/1/2026. The position is limited to 3/31/2030.

Topic
“Effect of multiple stress by genotype interactions on resource use efficiency, primary production and stress responses of maize”

This position is open to all qualified individuals (m/f/d) who are eager to engage in cutting-edge, interdisciplinary research at the interface of crop modelling, multiple abiotic-biotic stress interactions, ecological dynamics, and model integration and evaluation, contributing to the DFG-funded Research Unit “Concurrent multiple abiotic and biotic stress interactions in maize: impacts and mechanisms” (MultiStress RU 6101), pending final approval on 11 December 2025.

Project context: The DFG-funded MultiStress RU investigates how maize responds to multiple, interacting abiotic (e.g., drought, nitrogen deficit) and biotic (e.g., Setosphaeria turcica) stress factors. Through an integrated experimental–modelling approach, the project aims to improve our understanding and prediction of crop performance under complex stress environments.

This PhD position will focus on detailed phenotyping, including stress response-related traits as assessed in systematic studies conducted in the field and greenhouse, to investigate aboveground carbon (C) and dry matter partitioning and nutrient use efficiency including defence traits affecting aboveground biomass quality. The PhD position will engage closely with another position studying the belowground biomass allocation. The aim is to analyse the effects of Setosphaeria turcica or Ostrinia nubilalis stress on maize growth, yield, and quality, and its interactions with water and nutrient stress across temperate and tropical environments. It will also help integrating data for novel model routines (e.g. on interactions of stemborer with nitrogen and water deficits).

Main Tasks

• Jointly work on data collection of above- and belowground biomass traits in field trials and the greenhouse includingSample processing via organ-specific separation and subsequent sample processing and lab analyses for nutrient uptake and nutritive value
  1. non-destructive (e.g. SPAD, LAI, stomatal conductance) and
  2. destructive (biomass) data
• Analyse data statistically to explore how biotic and abiotic stresses jointly affect yield and quality and resource use efficiency under varying environmental conditions
• Collaborate closely with other subprojects within the Research Unit, linking experimental, modelling, and synthesis activities.
• Publishing your research in international peer-reviewed journals, presenting research on conferences and actively engage in project-related workshops

Required Qualifications:

• Master’s degree (or equivalent) in Crop Science, Agronomy, Agricultural or Environmental Sciences, Plant Pathology, Systems Biology, or a related field
• Strong interest in quantitative forage crop production and the interaction between plant physiology, environment, and disease
• Proven expertise in field sampling plants above- and belowground
• Proven experience in conducting greenhouse trials
• Strong laboratory analytical skills of plant biomass (forage value, isotopes, nutrients) including elemental analysis, NIRS, photometry, ICP
• Knowledge of maize morphology and its separation
• Extensive experience in soil sampling and further processing
• Extensive experience in measuring non-destructive plant traits
• Solid skills in programming and data analysis (e.g., R, Python)
• Experience with Microsoft office tools
• Ability to work independently and collaboratively in an interdisciplinary and international team.
• Very good command of English, both written and spoken.

We offer:

• A dynamic, interdisciplinary, and internationally leading and pioneering research environment within the DFG Research Unit MultiStress, which consists of research consortium of ten universities /research organisations from Germany, Kenya, Italy (+ CIMMYT), representing all important disciplines of crop science.
• Excellent supervision and training opportunities in field trial execution, data interpretation, and scientific communication.
• Access to unique multi-stress experimental datasets (and travel to associated experimental sites in Germany and Kenya)
• Access to high-performance computing infrastructure.
• Opportunities for unique international collaboration around maize and participation in versatile project thematic workshops and international conferences.
• a four-year PhD position (65% TV-L E13)

For further information, please contact:
PD Dr. Martin Komainda, Grassland Science, University of Goettingen, Germany, E-mail: martin.komainda@uni-goettingen.de or Prof. Dr. Johannes Isselstein, Grassland Science, University of Goettingen, Germany, Email: jissels@gwdg.de

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 1/25/2026 on the application portal of the university using this link: http://obp.uni-goettingen.de/de-de/OBF/Index/76198. For more information get in touch with Martin Komainda directly via E-Mail: martin.komainda@uni-goettingen.de, Tel. 0049551-3924388 .

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)

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