Heavy Goods Vehicle Driver (M/F)
The project is carried out within the ANItA (Academic-industrial Nuclear technology Initiative to Achieve a sustainable energy future) competence center. You will be part of a joint academic and industrial research environment with supervision from both Uppsala universitet and industrial partners. Through close collaboration with industry, you will have the opportunity to develop research methods with clear industrial relevance and contribute to knowledge exchange between academia and industry.
Reliable and fast fuel performance calculations are important for both today's reactors and future designs, including small modular reactors (SMR). The project builds upon the research conducted within CaNel – Calibration of Fuel Performance Codes, where methods based on statistical calibration, uncertainty quantification, and machine learning-based surrogate modeling have been developed for the simulation of nuclear fuel performance. You will further develop these methods to enable faster and more precise predictions with well-quantified uncertainties.
The project includes, among other things, calibration against time-dependent and axially resolved measurement data, transfer of uncertainties between coupled sub-models, and the development of time-dependent machine learning-based surrogate models. The models must be able to handle variations in, for example, fuel type, enrichment, and gadolinium content, be evaluated even outside the training range, and be applied to a broader selection of fuel designs and operating conditions. They shall also be demonstrated in industrially relevant applications, such as prediction of cladding tangential stress and assessment of the risk of PCI-related fuel damage.
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 the development, implementation, and evaluation of statistical and machine learning-based methods for the calibration and simulation of nuclear fuel performance.
The tasks include:
further developing calibration methods so that time-dependent and axially resolved measurement data can be used, developing methods to propagate uncertainties between calibrated and coupled sub-models and thereby avoid double-counting of effects, developing and evaluating time-dependent machine learning models for sequence-to-sequence prediction of fuel behavior, expanding surrogate models so that they can handle multiple fuel types, enrichment levels, and gadolinia concentrations as well as, if possible, predict uncertainties, generating and analyzing training and validation data for a wide spectrum of power histories and operating conditions as well as investigating the models' generalizability outside the training range, demonstrating the usefulness of the surrogate models in industrially relevant applications, for example for prediction of cladding tangential stress and assessment of PCI risk, implementing, testing, and documenting computational tools, for example in Python, and contributing to reproducible computational workflows, compiling, publishing, and presenting research results as well as participating 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 doctoral studies is the person who has
obtained a degree at the advanced level in technical physics, nuclear engineering, applied physics, energy technology, computational science, applied mathematics, statistics, machine learning, 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 the 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, statistics, 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.
Other Desirable/Meritorious Qualifications
Experience in one or more of the following areas is meritorious:
nuclear fuel, fuel performance modeling, reactor physics, or nuclear engineering, numerical modeling of heat transfer, material behavior, or solid mechanics, Bayesian inference, model calibration, or MCMC methods, uncertainty quantification, statistical modeling, or Gaussian processes, machine learning for time series, sequence-to-sequence models, or surrogate modeling, work with scientific computational programs, large simulation or measurement data, and high-performance computing, experience with version control and reproducible computational workflows.
Great importance will be placed on personal qualities such as analytical ability, initiative, precision, and motivation to conduct doctoral studies in close collaboration between academia and industry.
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.
Information about doctoral education, eligibility requirements, and admission rules can be found on the website of the Faculty of Science and Technology.
About the Application
Please attach transcripts 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 by agreement. Location: Uppsala, Sweden.
Welcome with your application no later than September 30, 2026, UFV-PA 2026/2448.
Please note that this is a shortened version of the advertisement. To see the full advertisement, please click on ”Apply here” or see Uppsala universitet's website for job advertisements.