Uppsala University
6 days ago
PhD student in nuclear fuel performance modelling using machine learning Uppsala University in Sweden
Degree Level
PhD
Field of study
Computer Science
Funding
Temporary full-time PhD employment at 100% with fixed salary. Teaching and other departmental duties may be included up to 20% of full-time employment. No stipend amount is stated.
Deadline
Sep 30, 2026
Country
Sweden
University
Uppsala University
How do I apply for this?
Sign in for free to reveal details, requirements, and source links.
Apply for this position
Keywords
Suggested positions
About this position
PhD student opportunity at Uppsala University in nuclear fuel performance modelling using machine learning, statistical methods, and uncertainty quantification.
The position is based at the Department of Physics and Astronomy, Division of Applied Nuclear Physics, and is part of a close collaboration between Uppsala University, Westinghouse, and Vattenfall through the competence centre ANItA. The project focuses on developing fast, robust, and reliable computational methods for current and future nuclear energy systems, including small modular reactors (SMRs).
Research topics include calibration of fuel-performance codes, propagation of uncertainties between coupled sub-models, temporal machine-learning surrogate models, sequence-to-sequence prediction, and industrially relevant applications such as cladding hoop stress prediction and PCI-failure risk assessment. The work combines physics-based modelling with data analysis, Bayesian calibration, Gaussian processes, and high-performance scientific computing.
Eligibility: a relevant Master’s degree or equivalent doctoral-entry qualifications in engineering physics, nuclear engineering, applied physics, energy engineering, computational science, applied mathematics, statistics, or machine learning. Applicants should have strong knowledge of physics, numerical methods, statistics and/or machine learning, good programming skills (e.g. Python, Julia, C++), independent working ability, collaboration skills, and excellent English.
Funding: temporary full-time PhD employment with fixed salary. Teaching and other departmental duties may be included up to 20%.
Deadline: 30 September 2026. Location: Uppsala, Sweden.
Apply via Uppsala University’s recruitment system and include your transcript, degree project, and supporting documents. The application uses questions instead of a cover letter.
Funding details
Temporary full-time PhD employment at 100% with fixed salary. Teaching and other departmental duties may be included up to 20% of full-time employment. No stipend amount is stated.
What's required
Applicants must meet doctoral admission requirements: a Master’s degree in engineering physics, nuclear engineering, applied physics, energy engineering, computational science, applied mathematics, statistics, machine learning, or another relevant field; or at least 240 higher-education credits including 60 credits at Master’s level and an independent project of at least 15 credits; or equivalent knowledge. Required are good knowledge of physics, numerical methods, statistics and/or machine learning, strong programming skills (e.g. Python, Julia, C++), ability to work independently and in a structured manner, good collaboration skills, and strong spoken and written English. Merits include experience in nuclear fuel, fuel-performance modelling, reactor physics, numerical heat transfer/material behaviour/solid mechanics, Bayesian inference, model calibration, MCMC, uncertainty quantification, Gaussian processes, time-series machine learning, surrogate modelling, scientific computing, large datasets, HPC, version control, and reproducible workflows.
How to apply
Prepare your application with transcript of records, a copy of your degree project, and any other supporting documents you want to include. Submit the application through Uppsala University’s recruitment system before the deadline. Answer the application questions in place of a cover letter.
More information can be found here
Ask ApplyKite AI
How do I apply for this?
Sign in for free to reveal details, requirements, and source links.