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that provides timely and effective support to real-life policy processes. The successful candidate will become part of an interdisciplinary team and support the development of data and model assets which are used
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present with neurodevelopmental deficits associated with Autism Spectrum Disorder and hyperphagia, and in healthy controls. We will be using a range of methods, including behavioural phenotyping, cognitive
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. System Integration: Integrating the DTs into material and energy balance equations
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attention on food safety, climate-resilient agriculture, and regulatory controls, accurate detection and risk assessment of such mycotoxins have become critical components of modern food science, toxicology
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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seeks to enhance the predictivity, accuracy and applicability of FEA for WA-DED, enabling more efficient design and control of large-scale additive manufacturing processes. The student will be based
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Grid Solutions Ltd on behalf of GE Vernova. The project’s topic will revolve around advanced high-voltage power electronics design and control, addressing both academic and industry needs. HVDC
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are evaluated in controlled settings and do not fully capture the realities of large-scale ecological applications. This PhD project will investigate Long-Tailed Open-Ended Semantic Segmentation (LTOESS), a
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into advanced turbofan configurations. This doctorate will research highly innovative technologies that carefully control the temperature of key engine components. The Oxford Thermofluids Institute
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that