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responses and mitigation scenarios across realistic European agricultural landscapes (illustrative example shown in Figure 1). There is also an opportunity to collect original movement and behavioural data
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, despite its widespread application, polygraph data capture and analysis has received limited systematic research and does not yet incorporate modern sensors, computing and analytical techniques. Project
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environment; opportunities to co-create your project; hands-on fieldwork and wildfire simulation experience; collaboration with leading researchers; and training in advanced characterisation, data analysis, and
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their suitability for real-time emergency decision-making. This project addresses this challenge by combining physics-based modelling with data driven surrogate approaches. The first stage of the project will involve
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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minimum English language requirements. Further details are available on the International website . Funding information: The studentship is for three years and provides a tax-free stipend of £20,780 per
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aspects of machine learning. Applications include improving the efficiency of data assimilation methods and understanding why and how deep learning works. Applicants should have, or expect to achieve
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postgraduate degree. For more information on English language requirements, please visit this page . Further details The School of Computing at the University of Kent and Huawei invites applications for 2 fully
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with bioprocess modelling and simulation tools (e.g., Aspen Plus, MATLAB, or Python), TEA, and LCA. Strong data analysis, problem-solving, and teamwork skills. Commitment to sustainability and research
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programmed in advance. If anything changes, it may fail. This project explores how to build more adaptable systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural