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networks of these devices we will use digital twins; machine learning models trained to predict physical systems but are differentiable. This project will advance the machine learning methods, particularly
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Supervisory Team: Hector Calvo-Pardo; Vahid Yazdanpanah; Tiago Alves (Solar Americas ); Enrico Gerding PhD Supervisor: Hector Calvo-Pardo Project description: Machine learning (ML) holds immense
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Overview Qualification type: PhD Subject area: Control and Machine Learning Location/Campus: College Lane, Hatfield Closing application date: 10 June 2024 Start date: July 2024 or as soon as
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usefulness of the forecast, and perception of forecast performance by the public. Statistical post-processing techniques can help to reduce forecast errors by training machine learning models on data sets
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learning called Machine Listening. The PhD project brings together world-leading audio experts to develop state-of-the-art techniques as a widely-applicable embedding of acoustic environments. Based in
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sustainability analysis through a machine learning (ML) and explainable artificial intelligence (XAI) outlook. The project marks a significant advancement in improving public safety against both low-probability
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, equipment, and travel related to the project. Overview Machine learning aims to transform the drug discovery landscape through the prediction of potential new therapeutics with unprecedented speed and
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EPSRC CASE Studentship. University of Sheffield and Syngenta Crop Protection Machine learning is increasingly used for decision making and molecular design in the pharmaceutical and crop protection
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PhD Studentship: Integrating computational physics-based simulation and machine learning with drug discovery pipelines Award Summary 100% home fees covered, and a minimum tax-free annual living
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Turing University Network) and the CitAI Research Centre (which features academic staff with extensive expertise in machine learning for healthcare). They will also be able to exploit the power of Hyperion