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, or Dr Dimitri Alexandre Rochman,

Please submit your application online by 30 April 2020 (including addresses of referees) for the position as a PhD Student (index no. 4805-00).

Paul Scherrer Institut Human Resources Management, Nicole Schubert, 5232 Villigen PSI, Switzerland