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Field
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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-resource settings. This project aims to achieve several objectives, including the development of a new AI-algorithm and a paired dataset for comparing how different imaging techniques influence
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candidates whose work lies at the intersection of statistics, machine learning, data analytics and modern AI algorithms. This includes, in particular, statistics for high-dimensional and complex data
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algorithms proficiency in both general-purpose programming (e.g., Python) and scientific computing, with a preference for experience in Julia Experience in writing up research work for publication Highly
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criteria Familiarity with the regulatory environment around Deep Learning or Machine Learning algorithms Experience applying quality system standards, software development standards and regulation, e.g. ISO
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, post-docs and interns collaborating across universities to build better algorithms, software tools and benchmarks to assess the safety of AI implementations at the software and hardware level. We
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formulation, which displays striking similarities to that used by the Computational Fluid Dynamics (CFD) community, has inspired the investigators to adopt conventional CFD algorithms in the novel context
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. By integrating cutting-edge multi-agent systems, federated learning, and game theory, this project will develop sophisticated decentralised algorithms that enable autonomous vehicles (AVs
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variants of importance sampling. We will connect these methods to modern formulations of Monte Carlo algorithms to improve their accuracy, scalability, and overall computational cost. The methodology so
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quantitative analysis skills and experience developing algorithms and/or conducting statistical analyses with biological datasets. Background and work knowledge in statistics, algorithms, optimization of novel