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testing approaches that can be used to verify that machine learning models are not biased. Required knowledge Software engineering, software testing, statistics, machine learning
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system to unlock important information from unstructured data sources including X-ray images, surgical and radiology text reports. We will compare prediction models based on existing, routinely collected
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for helping humans meet this challenge are causal Bayesian networks, which can accurately model complex probabilistic systems. However, because people are notoriously deficient in probabilistic reasoning
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events with the GOTO telescope network. Projects focussing on thermonuclear bursts will involve analysis of new and archival data from satellite-based X-ray telescopes, and running numerical models
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I specialise in the numerical modelling of high-energy particle collisions , such as those occurring at the Large Hadron Collider. Accordingly, most projects I offer straddle the intersection
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We live and work in a world of complex relationships between data, systems, knowledge, people, documents, biology, software, society, politics, commerce and so on. We can model these relationships
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The world is dynamic and in a constant state of flux, yet most machine learning models learn static models from a dataset that represents a single snapshot in time. My group's research is
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This project aims to harness big data from ubiquitous smartphone sensors to reduce the impact of road transport on the environment. Specifically, we’ll design novel data modelling and indexing
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, modeling uncertainty in data sources and queries, and exploiting rich information from several data sources. Required knowledge Required knowledge Essential First class Honors or Masters degree including
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. The latest advanced techniques in machine learning and computer vision for image content analysis will be applied to generate data for dynamic species distribution models. This data will in turn be used