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. To learn how to apply, go to the How to Apply page: westminster.ac.uk/research-degrees/how-to-apply . For useful resources to support your application, visit the Thinking of Doing a PhD page
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unit and then pre-processed data used as the input of the deep learning algorithm. The research will employ the SafeML tool (a novel open-source safety monitoring tool) to measure the statistical
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teaching assistantship. The ideal candidate will have a strong foundation in Python programming and hands-on experience with deep learning frameworks such as TensorFlow or PyTorch. Applicants with a
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, combining both accuracy and explainability; (3) extend statistical learning theory to offer theoretical bounds for intrinsically-aligned AI models; (4) employ the newly-developed metrics to train deep neural
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, willingness to learn, and the ability to think creatively about complex physical systems are just as important as specific technical expertise. This PhD project—High-Fidelity Simulations of Geological CO2
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identification context, while promising for network-level monitoring, has been largely underexplored. To this end, the project will explore the application of the next generation of deep learning algorithms, e.g
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expertise. This PhD project—High-Fidelity Simulations of Geological CO2 Sequestration (SIGECOS)—aims to advance our understanding of how supercritical CO2 behaves when injected into deep saline aquifers
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at scale raises privacy and real hardware constraint concerns. This PhD will focus on those challenges by developing a distributed, privacy-preserving NILM framework, so we can move from small research
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and interpretable deep learning models to upscale species-level mapping to regional satellite products. Organise co-creation workshops with local stakeholders and generate decision-ready indicators
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-agent reinforcement learning (MARL) framework for cyber-physical networked fault-tolerant control of renewable energy-fed smart grids under adversarial conditions [6]-[9]. Multiple autonomous agents will