Ph.D position “Uncertainty quantification and reliability analysis for noisy grey-box models”
The Chair of Risk, Safety and Uncertainty Quantification of ETH Zurich develops computational methods for managing the uncertainties in physical models used in various fields of engineering and applied sciences (civil and mechanical engineering and geosciences, among others). The Chair develops UQLab (www.uqlab.com), a comprehensive platform that gathers state-of-the-art algorithms for uncertainty quantification.
The Chair opens a Ph.D. position in the field of uncertainty quantification and active learning methods for grey-box models in the context of the European Project GREYDIENT (www.greydient.eu). This innovative training network aims at training a next generation of Early Stage Researchers (ESR) to fully sustain the ongoing transition of European personal mobility towards safe and reliable intelligent systems via the recently introduced framework of grey-box modelling approaches. One of the main challenges that we currently face in this context is the integration of the data captured from the plenitude of sensors that are involved in a particular road-traffic scenario, ranging from monitoring car-component loading situations to power network-reliability estimations. Grey-box models are an answer to this pressing issue, as they are aimed at optimally integrating (black-box) data-driven machine learning tools with (white-box) simulation models to greatly surpass the performance of either framework separately.
The objectives of this PhD are:
- to re-formulate the reliability problem in the context of noisy grey-box models,
- to apply sparse polynomial chaos expansions as a denoising tool,
- to combine denoising and active learning to obtain highly efficient reliability estimates for grey-box models based on expensive industrial models and noisy data.
The ideal candidate has a Master’s degree in civil/mechanical/electrical engineering or in computational sciences. Together with a strong background in scientific computing, he/she has proven experience in probability theory and statistics and some exposure to uncertainty quantification techniques (e.g. surrogate modelling, multi-fidelity simulation, global sensitivity analysis, structural reliability, etc.).
The candidate is familiar with developing scientific codes and has proven advanced Matlab/python programing skills. We are looking for highly motivated candidates who are self-driven, have excellent communication and writing skills (fluent spoken and written English is mandatory) and enjoy working in an interactive international environment with other PhD students, post-docs and senior scientists.
Applications with a comprehensive CV and a personal statement explaining why you are interested in this position, should be sent via the online application portal: www.greydient.eu/jobs. This position is ESR14. Applications via email or postal services will not be considered.
Further information can be found on our website www.sudret.ibk.ethz.ch. Questions regarding the position should be directed to Dr. Marelli (email@example.com).