PhD Student data assimilation

Bayesian Calibration Methodology for Complex Models

Your tasks

  • PhD thesis in Development and Demonstration of a Bayesian Calibration Methodology applicable to Complex Models (nuclear fuel behavior under irradiation)
  • Supporting teaching activities generally at EPFL
  • Setting up an advanced Bayesian Calibration methodology; its verification, validation and application to the envisioned complex models (fuel performance calculations)
  • Collaboration with OECD-NEA/NSC Expert Group on Multi-Physics Experiments, Benchmarking and Validation
  • Presentation of the scientific results at international conferences and publications in peer-reviewed journals
You will be enrolled in the doctoral school of the École polytechnique fédérale de Lausanne (EPFL).

Your profile

  • Higher education in nuclear engineering, mathematics or data science
  • Good knowledge of English
  • Programming skills in high level languages and scripts
  • Experience in modelling and simulations
  • Good communication and team work skills

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 Mathieu Hursin, phone +41 21 693 33 77 (EPFL) or Dr Alexander Vasiliev, phone +41 56 310 27 02.

Please submit your application online by 1 June 2022 (including addresses of referees) for the position as a PhD Student (index no. 4102-01).

Paul Scherrer Institut, Human Resources Management, Anita Bleiker, 5232 Villigen PSI, Switzerland