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Matias, Adrián Villaseñor, Rowena Jacobs Topic 4-Machine Learning for Causal Inference in Health Policy Assessment for Decision Making Supervisors - Dr Julia Hatamyar and Professor Andrea Manca Topic 5
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accelerators originally designed for artificial intelligence. These accelerators achieve exceptional performance by using low precision arithmetic, which is sufficient for machine learning tasks but much too
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of bacteria in the human gut, in-depth work with next-generation as well as 3rd generation sequencing technologies, then this PhD will be right for you. The ideal candidate will enjoy learning about metagenomic
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syndrome. Targeted projects currently include the following: Use AI/machine learning approaches to develop a means to quantify and classify tic movements and vocalisations in Tourette syndrome/tic disorder
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avenues may include linking plankton size patterns to krill dynamics, carbon export or nutritional quality, or developing tools for rapid ecosystem monitoring using machine-learning approaches
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properties of representative sediment classes. · Evaluate methods for predicting sediment type and physical properties from geophysical data using machine learning. · Assess the reliability
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properties of representative sediment classes. · Evaluate methods for predicting sediment type and physical properties from geophysical data using machine learning. · Assess the reliability
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erosion and subsequent effect on land-to-lake dynamics using isotope tracer and source apportionment methodology at test sites in the Winam Gulf. (2) Explore use of remote sensing data and machine learning
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personalised, ethnically-stratified risk scores. This is a highly interdisciplinary project at the intersection of machine learning, health equity, and precision medicine. The successful candidate will join a
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for complex data accessible to the scientific community and to produce innovative methodology related to trial designs, longitudinal and event history data, precision medicine, causal inference, AI/machine