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treatment processes through advanced machine learning, validated against physics-based models and experimental data. 2. System Integration: Integrating the DTs into material and energy balance equations
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reviewed journals (assessed at: application and interview) Ability to communicate clearly and effectively both in writing and verbally along with the ability to convey complex concepts to a range of
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structures. Extract meaningful physical insights from complex data. Collaborative Research: Work closely with the research team, including Conroy group members, to refine experimental approaches. Engage with
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that support more integrated preventative approaches to complex needs. This interdisciplinary project is ideal for students interested in applied data science, public policy, and improving outcomes
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million in losses each year, with landslides during earthquakes leading to over NZ$1 billion in damage for a single event. While landslide susceptibility modelling is an essential tool in risk management
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electronic health records, explore health usage related to HIV, TB, HBV and HCV for migrants to the UK. • adapt existing health economic models to estimate the cost-effectiveness of alternative infection
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This PhD opportunity at Cranfield University explores how next-generation AI models can be embedded within resource-constrained electronic systems to enable intelligent, real-time performance
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ambitious research team exploring quantum phenomena in large scale and complex systems, from high-temperature Bose-Einstein condensates to trapped solid-state particles in ultra-high vacuum. We combine
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, economic modelling, experiments, and presentation preparation, gaining broad exposure to the field of finance. The post will initially be offered for 12 months starting from September 2025 until August 2026
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address: Dr Fahad Panolan: f.panolan@leeds.ac.uk Project summary The Algorithms group at the University of Leeds (UK) is offering a fully funded 3.5-year PhD studentship on Parameterized Complexity and