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making and machine learning, with real-world testing and feedback. The successful applicant will work on decision making for anomaly detection, behaviour analysis and surveillance decisions, under
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. This includes sample preparation, data collection, computational analysis, and 3D structure determination of protein macromolecules. Experience in cellular tomography workflows for EM analysis of cells and
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, with proven experience of contributing substantially to preparation of manuscripts (literature review, analysis, interpretation, writing, submission) Experience contributing to the development
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are met. Outputs will include: Project scoping and problem analysis reports. Research publications in high-quality journals and conferences. This exciting role will contribute to the Centre for Augmented
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. Experience in one of more of: advanced optical imaging; classical imaging; multiphoton imaging; the development and application of numerical methods of deep learning or multivariate analysis. Interdisciplinary
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laboratory procedures for geochemical and isotopic analysis of geological materials. A strong research track record in one or more of the following earth science fields, including (but not limited
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). Proficiency in numerical modelling, data analysis and instrument control in languages such as Matlab, Python, C/C++, etc. Familiarity with sensor technologies and applications, machine learning, and electronics
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media; the development and application of numerical methods of image and signal analysis The ability to work well with people both in academia and Industry The ability to plan and manage own research
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metapopulation and/or individual based models Knowledge of Bayesian methods, including Approximate Bayesian Computation Experience with big data analysis and HPC environments Knowledge of additional programming