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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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Bezares (numerical relativity), Dr Stephen Green (gravitational waves, data analysis including machine learning, black holes), Dr Laura Sberna (gravitational waves, black holes, and environmental effects
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machine learning, data science, mathematics or a computational science), or a postgraduate qualification with a major statistical component. There is scope for the role to be undertaken in a hybrid manner
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proposals. Have a PhD in biostatistics or related subject with a numerate or computational component (including machine learning, data science, mathematics or a computational science), or a postgraduate
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postgraduate degree, ideally a PhD, in statistics, machine learning, or a related field. Experience of developing new statistical methods and a strong working knowledge of a statistical software package, such as
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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a role model and fostering an inclusive working culture. Person Specification PhD, or close to completion, in a relevant, quantitative field, e.g. meteorology, machine learning, climate science
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foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing can be
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with physical objects/environments, and audio rather than video based AR can help enhance memory/learning Humanizing Computer Mediated Communication: Synthesizing co-presence - What does it mean to feel
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web technologies Experience in teaching bioinformatics Previous experience with AI and/or machine learning approaches Interest in reproductive health and/or development of clinical tools and algorithms