Ph.D position “Active-learning multi-fidelity grey-box modelling”

Updated: 20 days ago
Job Type: Permanent

In der aktuellen Covid-19 Situation laufen die Rekrutierungen weiter. Es kann dabei allerdings zu Verzögerungen kommen. Vielen Dank für Ihr Verständnis.


100%, Zurich, fixed-term

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.


Project background

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.


Job description

The objectives of this PhD are:

  • to construct multi-fidelity models based on models with different levels of discretization, linearization, etc.,
  • to use active learning to optimally distribute the computational budget among different fidelity levels,
  • to include data-driven approaches in the multi-fidelity framework and use active learning to select from the combination of white-box, data-driven models,
  • to validate the developed methodologies on a case study at EDF, the French world leader in nuclear power generation.

Your profile

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.


ETH Zurich

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

Working, teaching and research at ETH Zurich
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