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disease patients using radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical
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to the present and modelling towards the future. Research within BEES is clustered within four general thematic areas: Ecology and Evolutionary Biology; Climate Science; Environmental Change, Sustainability and
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radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical imaging. Within our research
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manage research datasets, including development of analytic workflows, REDCap data collection tools, algorithm development, and validation of NLP pipelines · lead the development of scholarly outputs
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environments, fluent in R, while ideally have demonstrated experience in cancer or evolutionary genomics. Your key responsibilities will be to: conduct research, scholarly or professional activity independently
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key responsibilities will include several of the following: demonstrate research excellence in representation theory, algebra, combinatorics or related fields develop and document algorithms to assist
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applicant will work with the ReXIl team, AIML, and 4DMedical to turn data into clinical impact. They will be responsible for developing algorithms for image analysis, creating predictive models for disease
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, scalable numerical algorithms for extracting the evolution model of the relevant dynamical skeleton, quantifying associated uncertainties. We will develop mathematical theory underpinning the novel
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, group theory, and/or graph theory will be necessary. Experience in modelling biological processes, and in algorithm development or computation will also be valuable. Proven commitment to proactively
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group of experts to predict (probabilistically) whether these occupations will be automated, augmented or unaffected by emerging technologies. Using this data, a classification algorithm is then trained