Meet ApplyKite Twin, the application assistant that learns with you
Meet your Twin, it learns with you
Your Twin learns with you
Launch week codes:30% off annual Pro20% off monthly Pro
Ends in00h00m00s
Ends in00h00m00s
Marek Mutwil
Just Landed
New Today
Two PhD fellowships (MSCA Doctoral Candidates) in AI-driven discovery of plant metabolic pathways – knowledge graphs (DC7) and multi-omics networks (DC8) University of Copenhagen in Denmark
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Jan 31, 2027
Country
Denmark
University
University of Copenhagen
Apply for this scholarship
Keywords
Suggested scholarships
About this position
These two PhD fellowships are part of the Marie Skłodowska-Curie Doctoral Network Stripes&Stars at the Department of Plant and Environmental Sciences (PLEN), University of Copenhagen. The positions sit within an AI-driven effort to uncover plant metabolic pathways linked to flower and fruit colour, with a particular focus on specialised metabolites such as anthocyanins and other flavonoids in petunia, calibrachoa, poinsettia and bilberry.
The two doctoral candidates will form the computational backbone of the network at Copenhagen and work in close connection with experimental partners across Europe. The group develops large cross-species gene expression atlases, comparative co-expression networks, knowledge graphs and machine-learning tools to convert literature and omics archives into testable biological hypotheses. The research environment combines plant biology, data science and AI, supported by modern molecular biology laboratories, plant growth facilities and high-performance computing and GPU resources on the Frederiksberg Campus.
DC7 focuses on building a comprehensive knowledge graph for specialised metabolite pathways. The project will use large-language-model-based extraction to connect genes, enzymes, substrates, transporters, transcription factors, stresses and genetic modifications reported across the plant literature, then extend this resource to environmental context and reasoning-based hypothesis generation. The goal is to create a machine-readable framework that can surface missing pathway components, regulatory relationships and engineering targets, with an interactive interface for consortium use.
DC8 focuses on network-based discovery from multi-omics data. This project will construct explainable co-expression and regulatory networks from RNA-seq and complementary omics resources, integrate tissue- and cell-resolved evidence, and combine these results with the DC7 knowledge graph to support linked, interpretable pathway prediction. The outputs will help prioritise candidate regulators, enzymes and environmental cues for downstream validation by the experimental partners and shared consortium resources.
The doctoral candidates will also take part in network-wide secondments and training activities, including placements with partner institutions and workshops across the Doctoral Network. The principal supervisor is Associate Professor Marek Mutwil.
Funding details
Available
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 chemistry, biochemistry or molecular biology. Candidates should have strong analytical reasoning, good written and spoken English, and fulfil the MSCA mobility rule: they must not have resided or carried out their main activity in Denmark for more than 12 months in the 36 months before recruitment. They must not already hold a doctoral degree at the date of recruitment. Programming experience such as Python is an advantage, and relevant experience with NLP, LLMs, knowledge graphs, graph machine learning, transcriptomics or network inference is beneficial. An interest in plant metabolism is essential.
How to apply
Submit the application electronically via the APPLY NOW portal. Include a one-page motivation letter stating whether you apply for DC7, DC8 or both, answers to the four application questions, a CV, Bachelor’s and Master’s diplomas and transcripts, a publication list if possible, and reference letters if available.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the provider.
Professors
Associate Professor at University of Copenhagen
Denmark
Research Interests
Statistics
Artificial Intelligence
Biology
Computer Science
Omics
Professor
Denmark
Research Interests
Statistics
Chemistry
Biology
Omics
Large Language Models
Professor
Denmark
Research Interests
Statistics
Natural Language Processing
Chemistry
Computational Biology
Omics
Make your next move.
Prepare your application
Review drafts and confirm requirements with the provider.