The posting
The Department of Plant and Environmental Sciences (PLEN) invites applicants for two PhD fellowships in AI-driven discovery of plant metabolic pathways – doctoral candidates DC7 and DC8 of the Marie Skłodowska-Curie Doctoral Network Stripes&Stars. Both projects are part of the Marie Skłodowska-Curie Actions Doctoral Network Stripes&Stars (Grant Agreement No. 101312557), funded by the European Union under Horizon Europe. Stripes&Stars trains ten doctoral candidates across eight European countries in modern breeding of ornamental and fruit crops, using flower and fruit colour – anthocyanins and other flavonoids in petunia, calibrachoa, poinsettia and bilberry – as its scientific showcase. The network brings together seven beneficiaries (TU Wien, Klemm + Sohn, Università Campus Bio-Medico, Utrecht University, University of Copenhagen, Lantana Bio, UiT The Arctic University of Norway) and associated partners at Galab, the University of Helsinki, the University of Amsterdam and Chulalongkorn University. The University of Copenhagen hosts the network’s two data-science doctoral candidates (DC7 and DC8), who together provide the computational backbone for predictive colour and specialised-metabolite engineering.
Start dates: DC7 is expected to start 1 June 2027 (month 3 of the network) and DC8 1 January 2028 (month 10 of the network), or as soon as possible thereafter. Please state in your application which project you are applying for; you may apply for both.
Project DC7: Knowledge graph construction for specialised metabolite pathways (Stripes&Stars WP4–5) Flavonoids and anthocyanins colour petunia flowers and bilberry fruit, protect plants against UV light and stress, and attract pollinators – and roughly 20% of the carbon flux in plants passes through the flavonoid pathway. Yet knowledge of the enzymes, transporters and regulators that control which pigment accumulates where is scattered across tens of thousands of papers. This project will make that knowledge computable. You will:
- Use the PlantConnectome large-language-model pipeline to extract and interlink knowledge on enzymes, substrates, transporters, transcription factors, stresses and genetic modifications that regulate pigment and specialised-metabolite biosynthesis, from more than 70,000 publications, into the first comprehensive knowledge graph of plant specialised-metabolite pathways
- Extend the graph to environmental context – in particular how concurrent stresses (e.g. high light with heat or drought) reshape pigment and specialised-metabolite accumulation, and whether the regulators involved are shared across ornamental and fruit species
- Apply symbolic reasoning and link prediction (e.g. AnyBURL rule mining and graph neural networks) to infer missing pathway components and regulatory relationships, yielding testable hypotheses and engineering targets for the experimental partners
- Build an interactive, ChatGPT-like interface to the graph, so that consortium members and the wider community can retrieve complete, evidence-linked information on genes, enzymes, pathways and phenotypes
- Work closely with DC8 (network-based pathway prediction, also at UCPH) so that graph and expression evidence are integrated into one explainable framework
Secondments: TU Wien (Prof. Heidi Halbwirth, 3 months) to integrate graph predictions with enzyme characterisation and pigment pathway analysis, and Klemm + Sohn (Stuttgart, 2 months) to validate predicted targets in an applied breeding context. You will also take part in the network’s training programme of schools, workshops and transferable-skills courses.
Who are we looking for? We are looking for candidates within the field(s) of bioinformatics, computer science, data science or computational biology, or plant biochemistry with a strong computational interest. Experience with natural language processing, large language models, graph databases or knowledge-graph reasoning is an advantage. An interest in plant metabolism is essential; a formal background in it is not.
Project DC8: Multi-omics co-expression networks for predicting novel metabolic pathways (Stripes&Stars WP4–5) Which genes make a petunia petal purple, a poinsettia bract white, or a bilberry flesh blue? Many of the enzymes, transporters and regulators are still unknown, and the ones we know were found one at a time. This project will find them systematically from expression data. You will:
- Build advanced co-expression and regulatory networks from consortium and public RNA-seq data using the group’s TEA-GCN algorithm, which aggregates linear and non-linear correlations across conditions to yield condition-specific, explainable networks
- Integrate cell- and tissue-resolved expression, proteomic and epigenomic datasets to delineate the regulatory subnetworks that drive pigment and specialised-metabolite production
- Combine these networks with the DC7 knowledge graph and explore neurosymbolic reasoning – coupling learned graph representations (graph neural networks, knowledge-graph embeddings) with symbolic rules mined from the literature graph – to infer missing pathway members and regulators with explanations a biologist can check
- Predict and prioritise engineering targets (regulators, enzymes, environmental cues) for experimental validation by the network’s experimental partners, and follow how those predictions fare
- Consolidate network- and graph-based predictions into a shared resource for the consortium
Secondment: University of Amsterdam (Prof. Ronald Koes, 3 months) to integrate transcriptomic and epigenetic datasets from petunia. You will also take part in the network’s training programme of schools, workshops and transferable-skills courses.
Who are we looking for? We are looking for candidates within the field(s) of bioinformatics, computational biology, computer science or data science, or plant biology with a strong quantitative interest. Experience with transcriptomics, network inference, graph machine learning or knowledge graphs is an advantage. An interest in plant metabolism is essential; a formal background in it is not.
Eligibility and what we value MSCA eligibility rules This position is subject to the eligibility rules of the Marie Skłodowska-Curie Doctoral Networks:
- Mobility rule: you must not have resided or carried out your main activity (work, studies) in Denmark for more than 12 months in the 36 months immediately before the recruitment date.
- You must not already hold a doctoral degree at the date of recruitment.
- You may be of any nationality.
What we value Coding experience is welcome but not required at entry – we teach it. What we cannot teach quickly, and therefore select for, is scientific judgment:
- questioning data rather than accepting it – asking what was measured, how, and against which expectation
- noticing anomalies and treating them as information rather than noise
- organising several lines of investigation in parallel and knowing when to stop or redirect one
- connecting computational evidence to a concrete biological question
- changing your mind when the evidence changes
We are equally interested in candidates from biology who want to learn computation and candidates from computer science, physics or engineering who want to learn biology.
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 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. Our research facilities include modern molecular biology laboratories, plant growth facilities and dedicated high-performance computing and GPU resources.
Principal supervisor is Associate Professor Marek Mutwil, Department of Plant and Environmental Sciences (PLEN), University of Copenhagen. E-mail: [email protected].
The PhD programme A three year full-time study within the framework of the regular PhD programme (5+3 scheme), if you already have an education equivalent to a relevant Danish master’s degree.
Getting into a position on the PhD programme
Qualifications needed for the programme To be eligible for the PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g. chemistry, biochemistry or molecular biology. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database.
Terms of employment in the regular programme Employment as PhD fellow is full time and for maximum 3 years.
Employment is conditional upon your successful enrolment as a PhD student at the PhD School at the Faculty of SCIENCE, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant.
The terms of employment and salary are in accordance to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure.
Responsibilities and tasks in both PhD programme
- Carry through an independent research project under supervision
- Complete PhD courses corresponding to approx. 30 ECTS / ½ FTE
- Participate in active research environments, including a stay at another research institution, preferably abroad
- Teaching and knowledge dissemination activities
- Write scientific papers aimed at high-impact journals
- Write and defend a PhD thesis on the basis of your project
- Complete the secondments and network-wide training activities of the Stripes&Stars Doctoral Network
We are looking for the following qualifications
- Professional qualifications relevant to the PhD project
- Programming experience (e.g. Python) is an advantage; strong analytical reasoning is essential
- Relevant publications
- Relevant work experience, e.g. with NLP, LLMs, knowledge graphs or graph machine learning
- Other relevant professional activities
- A curious mind-set with a strong interest in making biological knowledge computable
- Good written and spoken English
- Fulfilment of the MSCA mobility and eligibility criteria stated above
Application and assessment procedure Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.
Please include:
- Motivated letter of application (max. one page), stating which project you are applying for (DC7, DC8, or both, in order of preference) and your motivation
- Written answers to the four questions below (max. 250 words each). You may use AI tools to help draft your answers; please say so if you do. The written answers are a starting point for the interview, where we will explore your reasoning in conversation.
- Curriculum vitae including information about your education, experience, language skills and other skills relevant for the position
- Original diplomas for Bachelor and Master and transcript of records in the original language, including an authorised English translation if issued in another language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted.
- Publication list (if possible)
- Reference letters (if available)
Application questions
- Research interest. Which biological question would you like to work on in this project, and what evidence would you need to answer it?
- Reading data. A gene has an expression value of 100 under control conditions, 200 under heat stress, 200 under drought, and 400 under heat and drought combined. What do you conclude, and what would you want to know before concluding it?
- Working in parallel. You have two weeks, a set of publicly available expression data, and a literature you do not yet know. How would you organise the two weeks, and what would you deliver at the end of each?
- Changing your mind. Describe a case where you believed something about a project or dataset and evidence made you revise it. What was the evidence?
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 deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified whether you will be passed for assessment. The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above-mentioned research area, techniques, skills and other requirements. The assessed applicants will have the opportunity to comment on their assessment. 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.
Questions For specific information about the PhD fellowship, please contact the principal supervisor, Associate Professor Marek Mutwil ([email protected]).
General information about PhD study at the Faculty of Science is available at the PhD School’s website: https://www.science.ku.dk/phd/.
The University of Copenhagen wishes to reflect the surrounding community and invites all regardless of personal background to apply for the position.
