Vacant job

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 employment and offers secure, advantageous working conditions? Welcome to apply for a doctoral position at Uppsala University.

As a doctoral 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 work environment is international and interdisciplinary, with close links between fundamental method development and technically relevant applications.

The project is a continuation of an ongoing doctoral project on core and fuel optimization for small modular reactors (SMRs) 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 doctoral project has developed methods for optimizing equilibrium cycles, with the goal of finding recurring fuel management strategies that provide good fuel economy while meeting reactor physics safety margins. Special 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, and extended 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 in core and fuel optimization faster and more reliably.

Job Responsibilities

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

The job responsibilities include:

• developing and applying machine learning-based surrogate models for reactor physics calculations,
• developing and evaluating optimization methods for fuel loading patterns and fuel composition,
• analyzing safety-related parameters such as reactivity, power distributions, fuel utilization, and margins to technical limits,
• working with large datasets from reactor physics simulations,
• implementing and documenting computational tools, for example in Python,
• compiling and publishing research results in scientific articles,
• presenting results at national and international conferences,
• participating in the research group's seminars, project meetings, and other scientific activities.

Teaching and other institutional duties may be included with a maximum of 20 percent of full-time employment.

Qualifications

Eligibility for doctoral studies is met by those who have

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

For the position, you also need:

• 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 systematically,
• good teamwork skills,
• good ability to express yourself in speech and writing in English.

Great importance will be attached to personal qualities such as analytical ability, initiative, accuracy, and motivation to pursue doctoral studies in an interdisciplinary field.

Desirable/Additional Merits

It is meritorious to have experience in one or more of the following areas:

• reactor physics, nuclear technology, 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 computing programs and high-performance computing,
• experience with version control and reproducible computational workflows.

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

About the Application

Please attach grade transcripts, a copy of your thesis, and any other documents you wish to present.

About the Position

The employment is fixed-term, according to HEO Chapter 5, Section 7. The extent is full-time. Start date January 1, 2027, or by agreement. Location: Uppsala.

Information about the position is provided by: Andreas Solders, 018-471 26 31, [email protected]

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 to submit your application by September 30, 2026, Ref. UFV-PA 2026/2129

Note that this is a shortened version of the advertisement. To see the full advertisement, please click "Apply here" or visit Uppsala University's website for job postings.