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Marek Mutwil

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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 flag

Country

Denmark

University

University of Copenhagen

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Keywords

Computer Science
Chemistry
Environmental Science
Biology
Natural Language Processing
Computational Biology
Omics
Epigenetic
Statistics
Bioinformatic
Transcriptomic
Large Language Models

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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.

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Professors

Associate Professor at University of Copenhagen

University Name
Country flag

Denmark

Research Interests

Statistics

Artificial Intelligence

Biology

Computer Science

Omics

Professor

University Name
Country flag

Denmark

Research Interests

Statistics

Chemistry

Biology

Omics

Large Language Models

Professor

University Name
Country flag

Denmark

Research Interests

Statistics

Natural Language Processing

Chemistry

Computational Biology

Omics