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greenhouses than constructing detailed CFD models for each location. Key expected innovations in this project are expected to include a hybrid data-driven and model-based predictive control approach that uses
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, these flows remain poorly understood. As a result, even the most basic properties cannot be predicted reliably. For instance, the best available models over- or underestimate the measured pressure drop in a
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Want to teach machines the mechanisms behind how the world changes — and build agents that act on them? Join us! World models are controllable, physics- and mechanism-grounded simulators of reality
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theory, control, and optimization to study the interaction of these factors. Some questions of interest include (but are not limited to): Modeling and analyzing strategic equilibrium problems with
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Explore trace element thermodynamics in high-temperature systems to control steel quality in the green transition—a PhD bridging experiments, modelling, and industrial impact. Job description The
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to predict traffic demand, user mobility, and network conditions, enabling autonomous decision-making that maximizes network performance and user experience. A unique aspect of this research is the integration
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traffic demands increase, there is a growing need for innovative methods to continuously assess track condition and predict deterioration. This PhD project addresses this challenge by developing a novel
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in development to ensure the materials are safe, affordable, and user-friendly. The project will also explore behavioural drivers, incentives, and innovative business models to stimulate adoption
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integrated early in development to ensure the materials are safe, affordable, and user-friendly. The project will also explore behavioural drivers, incentives, and innovative business models to stimulate
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, reliability, and operational performance. As railway infrastructure ages and traffic demands increase, there is a growing need for innovative methods to continuously assess track condition and predict