Ph.D positions in grey-box models for safe and reliable intelligent mobility systems

The goal of the project is to train the 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. This training network is led by KU Leuven and involves top academic institutions and industrial partners across Europe.

Interested candidates are encouraged to apply online through the webpage external pagehttps://www.greydient.eu/jobs/call_made. Candidates are supposed to specify up to 3 positions within GREYDIENT to apply for. Although the application deadline is not yet specified, it is recommended to apply until end of February 2021.

The Chair of Risk, Safety and Uncertainty Quantification at ETH Zurich offers 2 positions within GREYDIENT:

ESR 13 - Active-learning multi-fidelity grey-box modelling
The objectives are (1) to construct multi-fidelity models based on models with different levels of discretization, linearization, etc.; (2) to use active learning to optimally distribute the computational budget among different fidelity levels; (3) 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; (4) to validate the developed methodologies on a case study at EDF.

ESR 14 - Uncertainty quantification and reliability analysis for noisy grey-box models
The objectives are (1) to re-formulate the reliability problem in the context of noisy grey-box models, (2) to apply sparse polynomial chaos expansions as a denoising tool (3) to combine denoising and active learning to obtain highly efficient reliability estimates for grey-box models based on expensive industrial models and noisy data.