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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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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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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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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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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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organisations to participate in an engage with all aspects of the research process, ensuring equity and parity of esteem Working collaboratively with researchers and partners across C4, including in the other
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modifiers. The cultures will be analysed using basic molecular and cell biological techniqu es as well as high throughput imaging and analysis to observe modifier effects on LRRK2 and LRRK2 Rab substrate