Who is this page for?
This page is for students who are interested in doing a PhD related to
AI, software engineering, research software, scientific workflows, reproducibility,
digital research infrastructures, or decision support systems
.
You do not need to know the exact PhD topic already. We are happy to discuss possible directions
with students who have strong motivation, curiosity, and a good fit with AI4RSE.
Important funding note: CSC usually supports the student’s living costs.
Additional institutional costs, such as bench fees or matching funds, may still be required.
Therefore, every case needs to be discussed carefully before a formal supervision arrangement can be confirmed.
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 RSE
- AI assistants for coding, testing, refactoring, and documentation
- Repository review and software quality assessment
- Maintenance and evolution of scientific software
Agentic AI for software engineering
- Multi-agent systems for requirements, architecture, implementation, and testing
- Architecture-to-code and research-workflow-to-software pipelines
- Verification, conformance checking, and human oversight
FAIR software and research assets
- Software metadata and semantic linking
- 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, workflows, APIs, and scientific services
- Decision support for platforms, architectures, and infrastructures
Possible PhD topic directions
The topics below are examples. A final PhD proposal can be shaped together based on the student’s
background, CSC requirements, available supervision capacity, 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
From scientific papers to executable software
- Extract methods, datasets, parameters, and workflows from publications
- Reconstruct reproducible research software from scientific descriptions
AI search for research assets
- Build domain-aware retrieval systems over code, notebooks, datasets, workflows, models, services, and publications
- Improve discovery and reuse of scientific research assets
Knowledge graphs for FAIR research software
- Represent software metadata, dependencies, provenance, maturity indicators, and reuse opportunities
- Support machine-actionable research software catalogues
Decision support for AI and software 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
Who would be a good fit?
Background
- Computer science, software engineering, AI, data science, information systems, or a related field
- Interest in research software, scientific computing, or AI-enabled research
Technical skills
- Programming experience, preferably Python, JavaScript, Java, or similar
- Interest in software architecture, repositories, APIs, workflows, or data pipelines
Research attitude
- Curiosity, independence, and willingness to learn new tools and methods
- Ability to work carefully, document decisions, and communicate research clearly
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 degree programme and university
- Your research interests and why AI4RSE is interesting to you
- Your expected CSC application timeline
2Attach useful documents
- CV
- Transcript or grade overview, if available
- One-page research idea, if you already have one
- Links to publications, GitHub, portfolio, or thesis work, if available
3Discuss fit and feasibility
- We discuss research direction and supervision fit
- We check funding and institutional requirements
- If there is a good fit, we can explore the next steps together
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 CSC-funded 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