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Field
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machine learning and AI methods to develop clinical decision support systems for high-flow nasal cannula therapy. The project: Concerns around the possible negative consequences of delayed escalation
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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. You have a strong interest in machine learning/artificial intelligence. You have experience with deep learning. You have experience with reinforcement learning (preferred). You have experience with
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attention to quality, integrity, creativity and cooperation. You have a strong interest in machine learning/artificial intelligence. You have a strong interest in graph-based learning (e.g., graph neural
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move beyond conventional stimuli-responsive materials, which merely switch between predetermined states, toward life-like materials that genuinely learn: Forming memories, consolidating them over time
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About the project: Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy Supervisor: Dr Sara Sangtarash, University of Warwick Thermoelectric materials convert heat
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Development of machine and deep learning methods
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on information theory, machine learning, and control to analyse how local variability, sensor drift, and platform differences affect both global model performance and human supervisory factors such as workload and
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electric machines for future defence and aerospace propulsion systems. The research will explore innovative machine topologies and advanced multi-physics design methodologies to enable electric machines with
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PhD studentship: Discovery of rational therapeutic biomarkers in breast cancer by systems pathology and deep learning Supervisor: Dr Hamid Raza Ali Department/location:Cancer Research UK Cambridge