-
to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
-
of measurement systems, signal processing and analysis and the assessment of measurement accuracy, robustness and long-term stability. The resulting data form the basis for model-based approaches to evaluating
-
system models to analyse whether spatially coherent urban and energy configurations can be operated efficiently under realistic physical, spatial, and infrastructural constraints. The work aims to identify
-
the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
-
activation and micromechanical modeling Progressive damage modeling of reinforced FRPs Mechanical characterization and fracture experiments Complete a PhD thesis at ETHZ Your profile Highly motivated and
-
approaches. The PhD will develop and apply optimization-based energy system models to analyse whether spatially coherent urban and energy configurations can be operated efficiently under realistic physical
-
of electron spectroscopy experiments Modelling of experimental data Your profile The position is immediately available for a candidate with a master's degree (or equivalent) preferably in physics, physical
-
and flow field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision
-
: Study activation under NIR light and assess catalytic and photothermal performance. Biomedical evaluation: Validate efficacy in vitro astrocytoma relapse models and ex vivo patient-derived tissue samples
-
, technologies and systems. The ERAM group within TSL have great experience in SSbD, especially in combining different methods such as modeling mass flows analysis (MFA), Life cycle analysis (LCA) and semi