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Marek Mutwil
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PhD fellowship in single-cell gene regulatory networks of heat and drought responses in crops University of Copenhagen in Denmark
Degree Level
PhD
Field of study
Cell Biology
Funding
The project is financed by the Novo Nordisk Foundation through the Ascending Investigator grant “Accelerating crop resilience with AI-enabled gene function prediction and stress regulatory networks” (NNF26OC0113093).
Deadline
Jan 31, 2027
Country
Denmark
University
University of Copenhagen
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About this position
This PhD fellowship at the Department of Plant and Environmental Sciences, University of Copenhagen, sits at the intersection of plant stress physiology, single-cell biology, and comparative crop genomics. The project asks how heat and drought reshape gene regulation in individual cell types rather than in whole organs, where opposing responses can be hidden by averaging.
You will work on four major Nordic crops: rapeseed, potato, wheat, and barley. The experimental work is designed to resolve stress responses at single-cell resolution across leaves, roots, and floral meristems, and across early, middle, and late stages of response. The project combines single-cell RNA sequencing with chromatin accessibility profiling to identify regulatory programmes that are conserved across species and stress conditions.
The role is strongly experimental at the start: you will design and run the stress experiments, choose growth conditions and sampling strategies, perform phenotyping and physiological characterisation, and prepare nuclei or cells for single-cell sequencing. The generated libraries are produced on the group’s 10x Genomics platform and sequenced externally. After that, you will work on quality control, integration and cell-type annotation of the resulting atlases, and on inferring cell-type- and stress-specific regulatory relationships.
The project is embedded in the Mutwil group, which builds cross-species gene expression atlases, comparative co-expression networks and knowledge graphs, and uses AI methods to generate testable biological hypotheses. The group maintains community resources under the .plant.tools domain and works in a dynamic international research environment with modern molecular biology laboratories, plant growth facilities, and dedicated high-performance computing and GPU resources.
The fellowship also includes collaboration with Prof. Ryan Lister at the University of Western Australia on multimodal single-cell methods, and with stress-physiology expertise at PLEN Taastrup. A computational postdoc funded by the grant will work alongside you on multimodal integration methods, making this a highly collaborative project with strong experimental and computational components.
Overall, the position offers the chance to build single-cell atlases of crop stress responses and use them to reconstruct conserved gene regulatory networks across multiple species.
Funding details
The project is financed by the Novo Nordisk Foundation through the Ascending Investigator grant “Accelerating crop resilience with AI-enabled gene function prediction and stress regulatory networks” (NNF26OC0113093).
What's required
Applicants must have completed a degree equivalent to a Danish master’s degree related to the subject area of the project, such as plant sciences, biology, agricultural sciences, biotechnology, molecular biology or a related field. Hands-on experience with plant growth, abiotic stress experiments or physiological phenotyping is a strong advantage, and relevant work experience with molecular biology, RNA-seq or single-cell methods is also advantageous. Programming experience is not required because training in data science is provided. Good written and spoken English is required.
How to apply
Submit the application electronically via the APPLY NOW link. Include a motivated letter, answers to the four application questions, CV, diplomas and transcripts, and optionally a publication list and reference letters.
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Professors
Associate Professor at University of Copenhagen
Denmark
Research Interests
Statistics
Artificial Intelligence
Biology
Computer Science
Environmental Science
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