Ph.D position “Surrogate modelling for uncertainty quantification in offshore wind turbine design”

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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.

The Chair opens a Ph.D. position in the field of advanced simulation methods for offshore wind turbine design in the context of the European Project HIPERWIND. Aeroelastic simulators used in the industry require significant computational resources, even for a single simulation. However, due to the stochastic nature of the environmental conditions (wind and waves, varying both at the single turbine- and at the farm scale), large amounts of simulations are necessary to achieve turbine designs that can withstand the variable operating conditions. Surrogate models, Gaussian process regression in particular, are well-known tools to reduce the computational complexity associated to this type of analyses, but they cannot handle the high-dimensional input and output dimensions typical of aeroelastic simulations.

Within this project, the Ph.D. Student will develop dedicated high-dimensional surrogate models by combining dimensionality reduction tools from the machine learning literature and advanced surrogate models, and apply them to the fatigue assessment of the wind turbines. The Ph.D. student is expected to be a teaching assistant for a Bachelor course taught in German, and for this reason fluent spoken and written German is mandatory. The position is available as of February 15, 2021.

The ideal candidate has a Master’s degree in civil or mechanical 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 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.