This PhD thesis project will be an integral part of COLOSS. The aim is to develop surrogate models using the reference numerical simulations as basis and thereafter, apply the surrogate models for MLS uncertainty/sensitivity analyses and/or as loading optimisation method. This will contribute to an optimal experimental design (numerical simulations), constraints on design space (type and number of dependant variables) and accuracy versus computational performance of the employed methods
Your ideally have a sound background in theoretical or reactor physics, statistical & machine learning methods, numerical mathematics and be fluent in Python programming. Very good communication skills in English are required
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We perform cutting-edge research in the fields of matter and materials, energy and environment and human health. By performing fundamental and applied research, we work on sustainable solutions for major challenges facing society, science and economy. PSI is committed to the training of future generations. Therefore, about one quarter of our staff are post-docs, post-graduates or apprentices. Altogether, PSI employs 2100 people. The COLOSS project aims at an integral computational scheme for in-core/out-of-core spent fuel analysis, i.e. the development of so-called multidimensional loading surfaces (MLS). Considering the 5 nuclear power plants in Switzerland, about 12 000 spent fuel assemblies are expected for the final repository. In each canister for PWRs there will be 4 assemblies, hence the number of possible arrangements is over 10E21 possibilities. On this enormous amount of configurations boundary conditions w.r.t. safety i.e. effective criticality and maximum allowed decay heat, must be applied. On top of this the time dependent nature of the decay needs to be taken into account. In order to achieve a best-estimate-plus-uncertainty (BEPU) methodology for such MLS of storage/transport casks the COLOSS project will aim at deploying data science techniques as alternative to the computationally unaffordable forward monte-carlo based modelling and uncertainty propagation methods. For the Laboratory of Scientific Computing and Modelling we are looking for a PhD Student Combined Loading Optimization with Simulations and Surrogate models (COLOSS) Your tasks This PhD thesis project will be an integral part of COLOSS. The aim is to develop surrogate models using the reference numerical simulations as basis and thereafter, apply the surrogate models for MLS uncertainty/ sensitivity analyses and/or as loading optimisation method. This will contribute to an optimal experimental design (numerical simulations), constraints on design space (type and number of dependant variables) and accuracy versus computational performance of the employed methods. Your profile Your ideally have a sound background in theoretical or reactor physics, statistical & machine learning methods, numerical mathematics and be fluent in Python programming. Very good communication skills in English are required. We offer Our institution is based on an interdisciplinary, innovative and dynamic collaboration. You will profit from a systematic training on the job, in addition to personal development possibilities and our pronounced vocational training culture. If you wish to optimally combine work and family life or other personal interests, we are able to support you with our modern employment conditions and the on-site infrastructure. For further information please contact Dr Andreas Adelmann, firstname.lastname@example.org or Dr Dimitri Alexandre Rochman, email@example.com Please submit your application online by 30 April 2020 (including addresses of referees) for the position as a PhD Student (index no. 4805-00). 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