PhD student in core optimization with machine learning

Do you want to work with machine learning, optimization, and reactor physics, supported by competent and friendly colleagues in an international environment? Do you want to contribute to the development of advanced computational methods for future nuclear power systems? Do you want an employer that invests in sustainable employee engagement and offers secure, beneficial working conditions? Welcome to apply for a position as a PhD student at Uppsala University.

As a PhD student, you will be part of a research group working with reactor physics, fuel cycle analysis, and computational methods for core and fuel optimization. The group combines physics-based computational models with modern optimization and data analysis methods. The working environment is international and interdisciplinary, with a close connection between fundamental method development and technically relevant applications.

The project is a continuation of an ongoing PhD project on core and fuel optimization for small modular reactors, SMR, within the competence center ANItA (Academic-industrial Nuclear technology Initiative to Achieve a sustainable energy future). The competence center brings together academia and industry to strengthen Swedish nuclear technology competence and contribute to a sustainable energy transition. The previous PhD project has developed methods for the optimization of equilibrium cycles, where the goal is to find recurring fuel management strategies that provide good fuel economy while meeting reactor physics safety margins. Particular focus has been placed on combining advanced optimization algorithms with machine learning-based surrogate models, including graph-based representations of core loading patterns.

You will further develop this research direction. The project may, for example, include cycle-to-cycle optimization, development of new machine learning models, improved optimization strategies, uncertainty quantification, more efficient handling of physical constraints, as well as expanded analysis of fuel design, loading patterns, and safety-related quantities. The goal is to develop methods that make it possible to explore large design spaces within core and fuel optimization faster and more reliably.

Tasks

The tasks consist mainly of doctoral education, where you conduct research within the project and follow courses within the doctoral program. The work involves development, implementation, and evaluation of computational methods for core and fuel optimization using machine learning and optimization algorithms.

The tasks include:

develop and apply machine learning-based surrogate models for reactor physics calculations, develop and evaluate optimization methods for fuel loading patterns and fuel composition, analyze safety-related parameters such as reactivity, power distributions, fuel utilization, and margins to technical constraints, work with large datasets from reactor physics simulations, implement and document computational tools, for example in Python, compile and publish research results in scientific articles, present results at national and international conferences, participate in the research group's seminars, project meetings, and other scientific activities.

Teaching and other departmental duties may be included, up to a maximum of 20 percent of full-time employment.

Qualification requirements

Eligible for education at the doctoral level is the person who has

completed a degree at an advanced level in engineering physics, nuclear engineering, energy technology, machine learning, computer science, applied mathematics, or another area relevant to the project, or completed at least 240 higher education credits, of which at least 60 higher education credits are at an advanced level including an independent work of at least 15 higher education credits, or in some other way acquired substantially equivalent knowledge.

For the employment, the following are also required:

good knowledge of physics, numerical methods and/or machine learning, good programming skills, for example in Python, Julia, C++ or equivalent, good ability to work independently and in a structured manner, good collaborative skills, good ability to express oneself in speech and writing in English.

Great importance will be placed on personal qualities such as analytical ability, initiative, precision, and motivation to conduct doctoral studies within an interdisciplinary field.

Other desirable/merit qualifications

Experience in one or more of the following areas is meritorious:

reactor physics, nuclear engineering, or neutron transport, core optimization, fuel cycle analysis, or fuel management, machine learning, especially neural networks, graph neural networks, or surrogate modeling, optimization algorithms, for example evolutionary algorithms, stochastic optimization, or multi-objective optimization, uncertainty quantification or statistical modeling, work with scientific computational programs and high-performance computing, experience with version control and reproducible computational workflows.

Provisions for doctoral students can be found in the Higher Education Ordinance (Högskoleförordningen) Chapter 5, Sections 1–7, as well as in the university's rules and guidelines.

About the application

Please attach a transcript of records, a copy of your thesis, and any other documents you wish to invoke.

About the employment

The employment is time-limited, according to HF Chapter 5, Section 7. The extent is full-time. Commencement January 1, 2027, or as agreed. Location: Uppsala.

Information about the employment is provided by: Andreas Solders, 018-471 26 31, andreas.solders@physics.uu.se

In this recruitment, we have replaced the personal letter with questions that you answer in connection with your application. The answers will be used as part of the selection process.

Welcome with your application no later than September 30, 2026, UFV-PA 2026/2129

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