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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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Details The aim of this project is to combine nanomechanical methods with modelling (i) to develop quantitative, predictive models for the behaviour of molecules in sliding contacts, and (ii) to understand
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knowledge of process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support
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intelligent decision architectures, predictive analytics, and adaptive computational models that can operate in dynamic, uncertain, and high-stakes project environments. The appointee will conduct original
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. However, in many real-world and latency-critical applications, performance cannot be assessed solely through final recognition accuracy. Instead, the value of a prediction strongly depends on its timeliness
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methodologies, advanced controller synthesis, performance and stability assessment. Trade-off, prototyping and selection of advanced DFAOCS control methodologies: minimum set to be explored: model predictive
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accepted all year round Details Dynamic optimization is integral to many aspects of science and engineering, commonly found in trajectory optimization, optimal control (e.g. model predictive control, MPC
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field. This approach is related to data assimilation, allowing for better prediction, control, and optimisation of turbulent systems in engineering, energy, and environmental applications