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Field
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sound statistical models and methodology for the data-driven analysis of complex and dynamic systems, with the goal of enabling predictive, prescriptive, and pre‑emptive analytics. We work closely with
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challenge is therefore to develop efficient surrogate models capable of rapidly predicting macroscopic mechanical properties directly from microstructural descriptors while preserving the underlying physical
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
predictions for digital‑twin applications. Your responsibilities include: Development and implementation of thermo‑mechanical models in FEniCSx Integration of FEM models into neural operators and PINNs
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application! We are seeking a highly motivated PhD student to join a research project at the forefront of battery diagnostics and modelling, that will help shape the future of battery technology by developing
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differential equation models of bacterial persistence. A particular challenge, both for simulation and for machine learning, lies in the high dimensionality of these equations, which causes grid-based numerical
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and time-series modelling techniques will be used to predict delay and performance degradation. The predictive models will be integrated into a real-time robotic system and evaluated in realistic
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for new wind farms. In this context, accurately predicting the propagation of wind turbine noise in the atmosphere is essential to better understand the underlying physical mechanisms and to conduct
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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cohort and co-supervised by Prof. Andrew Kao, whose group will provide validated simulation models to benchmark the AI's prediction and Dr. Mikhail Poluektov. As the founding PhD student of the new BASE
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signatures in communication and coordination data predict collective outcomes — such as group spacing changes, behavioural synchrony, and leadership transitions — beyond what pairwise models capture