The posting
The Department of Plant and Environmental Sciences (PLEN) invites applications for a postdoctoral position in computational biology, focused on the integration of multimodal single-cell, epigenomic and literature-derived data into gene regulatory networks. The position 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).
The position is for 3 years. Start date is 1 March 2027 or as soon as possible thereafter.
The project Climate-driven heat and drought increasingly destabilise crop yields, yet the gene regulatory networks that govern stress responses in crops remain poorly resolved, because they have mostly been measured in whole organs. This programme will resolve them at the level of individual cell types and their cis-regulatory elements, in four major Nordic crops – rapeseed, potato, wheat and barley – under heat, drought and the two combined.
The postdoc is the computational integrator of the grant: the person who turns the data the grant generates into regulatory networks. The single-cell RNA-seq and ATAC-seq data are generated by a PhD student and a technician on the group’s 10x Genomics platform; your job begins when the sequencing reads arrive. This is a purely computational position – no wet-lab work is involved. Concretely, you will:
- Build the analysis backbone for the single-cell stress atlases: quality control, doublet and ambient-RNA handling, integration across four species, three stress conditions, three organs and three time points, and cell-type annotation using conserved markers – with pipelines the PhD students can run and extend.
- Integrate the modalities into gene regulatory networks. Infer transcription factor–target relationships by extending the group’s TEA-GCN method to single-cell data; identify cis-regulatory elements from accessible chromatin using known motifs, de novo motif discovery, convolutional neural networks and DNA language models; and link the two into TF–CRE–target triplets. Benchmark against SCENIC+, MAESTRO and related frameworks and adopt what works.
- Rank regulatory relationships by conservation across the four crops to obtain a high-confidence core, and develop the comparative methods this needs.
- Connect the networks to the rest of the grant’s work: to the group’s literature-derived knowledge graph and gene-function predictions, and to the open web platform through which the grant’s data, models and predictions are shared, so that regulators and targets can be prioritised for validation in rapeseed with traceable evidence.
- Co-supervise and train the PhD students and master’s students on the computational side, and help build the group’s single-cell and multi-omics analysis capacity.
Who are we looking for? You hold a PhD in bioinformatics, computational biology, genomics, plant molecular biology or a related field, and you have analysed single-cell or other high-dimensional omics data independently. We are looking for a computational scientist who can take raw multimodal data through to a defensible biological claim, and who is comfortable working alongside the experimentalists who generate the data.
Essential:
- A PhD (or a thesis submitted by the start date) in a relevant field
- Documented experience with the analysis of single-cell RNA-seq and/or single-cell ATAC-seq data, or with comparable multimodal genomic data
- Strong programming proficiency in Python or R, and experience building reproducible analysis workflows (e.g. Snakemake/Nextflow, version control, containers)
- A publication record demonstrating independent scientific contribution
- Excellent written and spoken English
Advantageous:
- Experience with gene regulatory network inference, motif analysis or cis-regulatory element prediction
- Experience with deep-learning models of regulatory sequence (CNNs, DNA language models) or with graph-based methods
- Experience with comparative or cross-species analysis of transcriptomic data
- Experience with plant abiotic stress biology or with crop species
- Experience co-supervising students
Our group and research – and what do we offer? The Mutwil group at PLEN works at the interface of comparative and evolutionary plant biology and AI-driven biological discovery. We build large cross-species gene expression atlases, comparative co-expression networks and knowledge graphs, and we use machine learning and large language models to turn the plant literature and omics archives into testable hypotheses. The group comprises several PhD students, postdocs and master’s students, and maintains a portfolio of widely used community resources under the .plant.tools domain, including PlantConnectome, CoNekt, diurnal.plant.tools and evorepro.plant.tools.
The postdoc will work closely with the two PhD students funded by the same grant (literature-derived knowledge graphs and gene-function prediction; single-cell stress experiments) and with collaborators Prof. Ryan Lister (University of Western Australia, multimodal single-cell genomics) and Prof. Zoran Nikoloski (University of Potsdam, regulatory network inference).
The group is part of the Department of Plant and Environmental Sciences (PLEN), Faculty of Science, University of Copenhagen. We are located on the Frederiksberg Campus in Copenhagen. We offer creative and stimulating working conditions in a dynamic and international research environment. Computational resources include a 196-thread/1 TB RAM workstation and guaranteed GPU access through LUMI and uCloud.
Inquiries about the position can be made to Associate Professor Marek Mutwil, [email protected].
Terms of employment The position is covered by the Memorandum on Job Structure for Academic Staff. Terms of appointment and payment accord to the agreement between the Ministry of Taxation and The Danish Confederation of Professional Associations on Academics in the State. Negotiation for salary supplement is possible.
Application procedure Your application, in English, must be submitted electronically by clicking APPLY NOW below. Please include:
- Motivated letter of application (max. one page)
- Curriculum vitae, including information about your education, experience, language skills and other skills relevant for the position
- Diplomas (Master and PhD degree or equivalent). If the PhD is not yet awarded, documentation of the expected date of the defence
- Complete publication list
- Separate reproductions of up to three particularly relevant papers
- Research statement (max. two pages) describing your previous work and how you would approach the multimodal integration and network-inference tasks described above
- Names and contact details of two or three references
The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.
Application deadline: The deadline for applications is 31 January 2027, 23:59 GMT +1.
We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.
The further process After the expiry of the deadline for applications, the authorised recruitment manager selects applicants for assessment on the advice of the Interview Committee. Afterwards an assessment committee will be appointed to evaluate the selected applications. The applicants will be notified of the composition of the committee and the final selection of a successful candidate will be made by the Head of Department, based on the recommendations of the assessment committee and the interview committee. The applicant will be assessed according to the Ministerial Order no. 242 of 13 March 2012 on the Appointment of Academic Staff at Universities.
You can read about the recruitment process at http://employment.ku.dk/faculty/recruitment-process/.
Interviews with selected candidates are expected to be held in February 2027.
