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or molecular biology with a basic knowledge of mitochondrial biology. Previous experience in cell culture, imaging and CRISPR screening in mammalian cells is a strong advantage. Experience with
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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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ligase activation Characterize the polyubiquitin architectures assembled and identify their downstream effectors Develop novel E3 ligase activity-based imaging technologies This position offers a unique
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and budgets. Engaging with the wider context Enhancing your contribution to the organisation through an understanding of the bigger picture and showing commitment to organisational values. Developing
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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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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
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML