## Your tasks ## * Design and implement the uncertainty analysis study for severe accidents in nuclear power plants (includes running sets of accident simulation codes such as MELCOR, MAAP) * Develop and evaluate different Bayesian Network models (from different structural learning and quantification algorithms) for surrogate modelling * Demonstrate the application of these models for two major use cases for nuclear Probabilistic Safety Assessment PSA: examining the impact of uncertainties on the PSA outcomes of interest for model validation and integrating uncertainty analysis findings in PSA models to optimize the structure of accident progression models and their success criteria ## Your profile ## * Master-s degree in nuclear engineering or other engineering disciplines (e.g. mechanical, chemical) with knowledge of nuclear systems * Familiarity with several accident computational codes, especially MELCOR * Familiarity with computational programming (e.g. use of Python, R, Matlab) * Familiarity with machine learning techniques or causal discovery algorithms is ideal, but not a requirement * Very good knowledge of English is required, written and oral ## 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. The work will be carried out at the Paul Scherrer Institute, in Villigen, Switzerland. The PhD degree will be awarded by the Swiss Federal Institute of Technology Zurich ETHZ. For further information, please contact Dr Luca Podofillini, e-mail, phone 41 56 310 53 56 or Dr Mateusz Malicki, e-mail, phone 41 56 310 21 27. Please submit your ap...