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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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responsible for data/image acquisition, analysis and interpretation and for using this information to design efficient heterogeneous photocatalytic processes. This will involve working on the synthesis and
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those leading to dementia and neuroinflammation. We have a strong focus on mechanistic dissection of genetic, molecular cellular and neuropathological processes which underlie across the neurodegeneration
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techniques, image analysis, genetic manipulations, biophysical approaches, and collaborate with theoreticians to address these questions. The specific details and aims of the project will be driven by
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sufficient to carry out image processing and data analysis. Skills in hypothesis-testing, experiment design, analysis of data sets and writing scientific manuscripts and reports. Demonstrate enthusiasm
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(see below). There is currently one fellowship available where the successful candidate will join one of our Cardiovascular Research Teams, details as follows: - BRC Theme: Cardiovascular / Imaging
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services, as well as receive clinical training in inherited cardiac conditions, sports cardiology and cardiac imaging. Person Specification Applicants should be clinically qualified with ALS and MRCP
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etching. Use ‘zoom’ tomography and imaging to resolve structural variation across scales from 30μm down to 3nm to establish a platform for reverse bottom-up enamel remineralisation. Bottom-up multi-modal 4D