External PhD candidates at Wageningen University & Research
An external PhD trajectory can be suitable for candidates who want to conduct doctoral research with academic supervision from WUR,
while being funded by another source or conducting the research alongside work or other professional responsibilities.
At WUR, externally financed PhD candidates are normally employed by another institute or organization, while external PhD candidates
may conduct their research in their own time. In both cases, the main academic relation with WUR is through the supervisory team.
Important note: contacting us does not guarantee PhD admission, supervision, funding, visa support, or institutional approval.
A possible trajectory depends on academic fit, supervision capacity, funding feasibility, admission requirements, institutional procedures,
and the formal approval process at Wageningen University & Research.
Who is this page for?
This page is for candidates who are interested in a PhD related to
AI, software engineering, research software, scientific workflows, reproducibility, digital research infrastructures,
decision support systems, knowledge graphs, or trustworthy AI-enabled science
, and who can bring their own funding or funding arrangement.
Self-funded candidates
- Candidates who can independently support their PhD trajectory
- Candidates who have a clear research plan and sufficient time for doctoral research
Scholarship-funded candidates
- Candidates funded by national scholarship schemes, foundations, ministries, or international programmes
- Candidates who need a supervisor or host group before submitting a funding application
Employer-funded candidates
- Candidates employed by a university, research institute, public organization, or company
- Candidates whose employer supports a PhD as part of professional development or research collaboration
Company-sponsored candidates
- Candidates working on industry-relevant AI, software engineering, data, or digital infrastructure problems
- Candidates who want to connect academic research with real-world software-intensive practice
Official WUR PhD information
Before contacting us, please review the official WUR PhD information. These pages explain candidate types,
admission requirements, required documents, and the general PhD registration procedure.
International candidates may need to satisfy Dutch immigration and residence permit requirements. Funding, living allowance,
tuition fees, bench fees, employment status, and hosting arrangements must therefore be checked carefully before a formal PhD trajectory can start.
Research direction: AI for Research Software Engineering
Research software is more than code behind a publication. It carries scientific assumptions, data transformations,
workflows, models, decisions, provenance, and community practices. Our research investigates how AI can help researchers
design, test, document, maintain, evaluate, and evolve research software, while also making AI systems more reproducible,
explainable, secure, energy-aware, and trustworthy.
AI-assisted research software engineering
- AI assistants for coding, testing, refactoring, documentation, and maintenance
- Repository analysis, software quality assessment, and research software review
- Empirical studies of AI-assisted development in scientific contexts
Agentic AI for software engineering
- Multi-agent systems for requirements, architecture, implementation, and testing
- Human-in-the-loop, human-on-the-loop, and supervised autonomous workflows
- Verification, conformance checking, and responsible automation
FAIR software and research assets
- Software metadata, semantic linking, and machine-actionable software catalogues
- Knowledge graphs for code, workflows, datasets, models, and publications
- Search and recommendation for reusable research assets
Trustworthy AI-enabled science
- Testing, validation, explainability, privacy, and security
- Audit trails, provenance, and confidence-building mechanisms
- Reproducible and maintainable AI research pipelines
Green AI and sustainable software
- Energy-aware model, package, and infrastructure selection
- Software maturity, technical debt, and lifecycle management
- Sustainable software stewardship for scientific communities
Research infrastructures
- AI-enhanced virtual research environments
- Cloud, edge, HPC, APIs, workflows, and scientific services
- Decision support for platforms, architectures, and infrastructures
Possible external PhD topic directions
The topics below are examples. A final PhD proposal can be shaped together based on the candidate’s background,
funding model, available supervision capacity, employer or sponsor interests, and the strategic direction of AI4RSE.
Agentic AI for research software development
- Design AI agents that support coding, testing, documentation, review, and maintenance
- Study how human researchers and AI agents collaborate in software-intensive science
AI-assisted software engineering in industry and science
- Evaluate how generative AI changes development workflows, quality assurance, and maintenance
- Develop governance and evaluation methods for AI-assisted software work
Knowledge graphs for FAIR research software
- Represent software metadata, dependencies, provenance, maturity indicators, and reuse opportunities
- Support machine-actionable research software catalogues and software observatories
Decision support for AI, software, and infrastructure choices
- Develop evidence-based models for selecting packages, platforms, architectures, models, and infrastructures
- Support transparent and explainable technical decision-making
Technical debt and software maturity in science
- Detect architectural erosion, dependency risk, documentation debt, and reproducibility gaps
- Design maturity models and AI-supported quality assessment tools
Responsible, trustworthy, and sustainable AI systems
- Study reproducibility, explainability, fairness, privacy, security, and energy awareness
- Develop evaluation frameworks for trustworthy AI-enabled research workflows
What we expect from external PhD candidates
Academic background
- A relevant master’s degree or equivalent academic preparation
- Strong fit with computer science, software engineering, AI, data science, information systems, or a related field
Time and commitment
- Realistic availability for a multi-year PhD trajectory
- Ability to plan research activities, meetings, writing, experiments, and publication work
Research proposal
- A clear initial research idea, problem statement, and motivation
- Alignment with AI4RSE themes and WUR academic standards
Funding and support
- Evidence of self-funding, scholarship funding, employer support, or company sponsorship
- Clarity about costs, employment status, visa requirements, and institutional responsibilities
How to contact us
If you are interested, please send a short email before preparing a full proposal. This helps us check the fit,
funding situation, possible topic direction, and supervision capacity.
1Send a short introduction
- Your current role, university, company, or organization
- Your academic background and research interests
- Your motivation for an external PhD in AI4RSE at WUR
2Explain your funding model
- Self-funded, employer-funded, scholarship-funded, company-sponsored, or another arrangement
- Expected funding period and available time for research
- Any visa or residence permit considerations, if relevant
3Attach useful documents
- CV
- Transcript or diploma overview, if available
- One-page research idea or draft proposal
- Links to publications, GitHub, portfolio, thesis work, or relevant professional projects
Frequently asked questions
Can I do an external PhD at WUR while working elsewhere?
This may be possible if the research topic, time commitment, funding arrangement, employer support, and supervision capacity are realistic.
The formal process must follow WUR procedures and admission requirements.
Can a company sponsor my PhD?
A company-sponsored PhD may be possible when the topic has academic depth and fits AI4RSE research themes. The project must remain suitable
for independent scientific research and must satisfy WUR requirements.
Do I need a complete proposal before contacting you?
A complete proposal is not required for the first email. A short one-page research idea is useful, especially if it explains the problem,
research motivation, expected contribution, and possible connection to AI4RSE.
Does WUR provide salary or funding for this route?
This page is intended for candidates who already have, or are actively arranging, their own funding. WUR employment-funded PhD vacancies are normally
advertised separately through official vacancy channels.
Supervisors in AI4RSE
Dr. Siamak Farshidi
Assistant Professor at the Information Technology Group,
Wageningen University & Research, and co-chair of the AI4RSE Lab.
His research focuses on AI for research software engineering, decision support systems,
generative AI applications, software architecture, recommender systems,
and reproducible AI-enabled science.
- Point of contact for external PhD inquiries in AI4RSE
- Research interests include agentic AI, generative AI, FAIR software, decision support, and trustworthy AI-enabled science
- Email: siamak.farshidi@wur.nl
WUR profile