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Position: PhD Student
Institution: Paul Scherrer Institut
Location: Villigen, Aargau, Switzerland
Duties: 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
Requirements: 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
   
Text: Skip to main content DE EN FR MENU Search Search Search Hauptnavigation Labs & User Services Open mainmenu item Overview Research at PSI Research Divisions and Labs Facilities and Instruments Research Initiatives PSI User Labs Scientific Highlights Scientific Events Scientific Career Useroffice Visitors Open mainmenu item Overview Contact How to find us Public Events Visitor Centre psi forum SchĂ¼lerlabor iLab Center for Proton Therapy Industry Open mainmenu item Overview Technology Transfer Spin-off Companies PARK innovAARE Our Research Open mainmenu item Current topics from our research Matter and Material Human Health Energy and Environment Large Research Facilities General Brochures Films Media Corner Career & Further Education Open mainmenu item Job Opportunities Personnel Policy Working at PSI Equal Opportunities, Diversity & Inclusion Training and Further Education Vocational Training PSI Education Centre Support Program "PSI Career Return Program" PSI-FELLOW/COFUND About PSI Open mainmenu item PSI in brief Strategy Guiding principles Facts and figures Organisational structure Suppliers and customers Customers E-Billing IT and Computing Safety at PSI (in German) Search Search You are here: PSI Home Education and Jobs Job Opportunities PhD Student Secondary navigation Education and Jobs Job Opportunities Expanded submenu item Our application process at a glance Jobmail Spontaneous application Further Positions in the ETH Domain Personnel Policy Open submenu item Principles Collaboration and leadership Human Resources Development HRS4R Working at PSI Open submenu item Employment Conditions Social Benefits Further positive points Corporate Social Responsibility Training and Further Education Open submenu item Higher Education Vocational Training School Lab High-school matricu­lation projects PSI Education Centre Support Program "PSI Career Return Program" Open submenu item Eligibility Criteria Members of the evaluation committee PSI-FELLOW/COFUND - International Fellowship Programme for Postdocs Useful links Equal Opportunities, Diversity & Inclusion Homepage PSI Education Centre Homepage Vocational Training Homepage Career Center The Paul Scherrer Institute PSI is the largest research institute for natural and engineering sciences within Switzerland. 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, andreas.adelmann@psi.ch or Dr Dimitri Alexandre Rochman, dimitri-alexandre.rochman@psi.ch 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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