Publisher
source

Uppsala University

PhD in Nuclear Fuel Performance Modelling and Machine Learning Uppsala University in Sweden

Degree Level

PhD

Field of study

Computer Science

Funding

Full funding available

Deadline

Sep 30, 2026

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Country

Sweden

University

Uppsala University

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Keywords

Computer Science
Mechanical Engineering
Materials Science
Mathematics
Nuclear Engineering
Computational Science
Uncertainty Analysis
Applied Physics
Statistical Modelling
Physics
ML

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About this position

Uppsala University is advertising a PhD position in Nuclear Fuel Performance Modelling and Machine Learning within the Division of Applied Nuclear Physics and the ANItA competence centre. The project combines nuclear engineering, applied physics, machine learning, statistical modelling, scientific computing, and physics-based simulations to develop fast and reliable methods for nuclear fuel-performance analysis.

The research aims to build surrogate models and calibration/uncertainty-quantification methods for simulations relevant to current nuclear reactors and future systems, including SMRs. The position is carried out in a strong research environment at Uppsala University in close collaboration with Westinghouse Electric Company and Vattenfall.

Location: Uppsala, Sweden.

Ideal background: engineering physics or nuclear engineering; applied physics, mathematics, or computational science; machine learning, statistics, or scientific computing; numerical modelling and simulation. Strong programming skills and interest in real engineering applications are emphasized. Prior nuclear engineering experience is helpful but not required.

Deadline: 30 September 2026.

Start date: 1 January 2027 or as agreed.

Interested candidates should apply through the university’s application portal linked in the post.

Funding details

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

How to apply

Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.

More information can be found here

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