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Field
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cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy
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validation of algorithms using these databases, and related data analysis. The ideal candidate will have a strong understanding of SQL and expertise in programming languages such as Python and/or R. Main
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EPSRC-funded project, MAPFSI that will be focused on developing experimentally-validated computational algorithms for fluid-structure interaction problems including multiphysics effect of electromagnetism
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for Science & Technology (KAIST), and an external stay at KAIST will be included as part of the PhD program. Qualifications Proficiency with Python Experience implementing various Machine Learning algorithms
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Python, data structures and algorithms, database systems and data management, cloud computing for data science, advanced data visualization, and machine learning. As a liberal arts college, we value
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applications. Our overarching aim is to obtain a holistic view of interconnected biological systems in health and disease. We develop clearing technologies for cellular-level imaging and deep learning algorithms
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to test the current algorithm and inform development of future algorithm refinements aimed at supporting diabetic foot ulcer (DFU) prevention through identification of temperature differences of > 2.2°C
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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terrestrial system models, for example using data analysis methods, such as data assimilation, physical- or process-based machine learning, or deep learning algorithms Analysis of the effects of human
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, and use smart phone apps to collect passive and active data using a prospective observational cohort study design. We will use this data to develop and validate a personalised risk prediction algorithm