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such as antenna arrays; and the modelling of advanced electromagnetic structures such as metasurfaces, which allow for unconventional control and manipulation of electromagnetic fields. Students who enjoy
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, or predictive modeling—based on real experimental data. You will work closely with engineers, technicians, and the postdoc to build and refine data pipelines and interfaces. As part of your research training, you
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small-scale processing sector. By joining this project, you will contribute to the development of AI-powered tools that predict non-compliance, improve food safety monitoring, and ultimately protect
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/SinglePageApplicationForm.aspx… Requirements Research FieldComputer scienceEducation LevelPhD or equivalent Skills/Qualifications Professional skills Experience in: Reinforcement Learning (RL), Model Predictive Control (MPC
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, the CAPeX approach to finding new electrocatalytic materials for energy conversion reactions uses state-of-the-art machine learning techniques, but experimental feedback is needed to improve the models and
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are expected to be significant in: Earth system science – by improving models of Earth surface evolution and enabling better predictions of landscape response to climate change. Engineering and applied physics
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that can then be tested quickly in the lab rather than remain computational predictions? Do you also wish to work closely with experimental biologists and gain a solid grasp of how experimental work is
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and operation of building HVAC systems, these technologies support both energy efficiency and flexible demand objectives. Model predictive control (MPC), which involves physics-based building energy