Overview
Uppsala University is recruiting a full-time PhD student to develop machine-learning and optimisation methods for nuclear-reactor core and fuel optimisation, including applications to small modular reactors. The project combines reactor physics, surrogate modelling, computational optimisation and data-driven methods.
Complete research opportunity details
The doctoral project is based in Applied Nuclear Physics and continues research within the ANItA competence centre. Work may include machine-learning surrogate models for reactor-physics calculations, optimisation of fuel loading and composition, uncertainty quantification, safety-parameter analysis, large simulation datasets and scientific software development. The employment is temporary and full-time, with a planned start on 1 January 2027 or as agreed.
Eligibility requirements
Applicants must meet Uppsala University's doctoral-study entry requirements, including a relevant master's degree or equivalent qualifying higher-education credits. The vacancy also requires good knowledge of physics, numerical methods and/or machine learning, programming skills such as Python, Julia or C++, ability to work independently and collaboratively, and good spoken and written English.
Tips for this phd opportunity
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- Match your research background to the project, laboratory, supervisor and required methods rather than applying only from the title.
- Prepare a focused academic CV, transcripts, publications and research statement or proposal when requested by the official call.
- Confirm funding duration, stipend/salary, tuition coverage, start date and eligibility for international applicants.
- Contact a supervisor before applying only when the official instructions recommend or require it.
Verification notes
Verified on the exact Uppsala University vacancy and current jobs index. The official page confirms reference UFV-PA 2026/2129, Department of Physics and Astronomy, Uppsala placement, full-time doctoral employment, planned January 2027 start and deadline 30 September 2026. Repository search for the exact reference found no existing canonical record before creation.
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