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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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in several tropical ocean locations. A successful candidate should have: A PhD (or equivalent) in geochemistry. Experience in the operation of single and/or multi-collector plasma mass spectrometer
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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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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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-The-Shelf (COTS), all-digital radar systems linked to an array of ultra-stable, ultra-low phase noise oscillators. The project is part of the UK Quantum Technology Hub in Sensing, Imaging and Timing (QuSIT
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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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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
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. Our team is closely linked to the Magnetic Resonance and Precision Imaging community at the Nottingham NIHR Biomedical Research Centre. Applicants must be highly motivated and self-driven, with a PhD or
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(MDres or PhD). Responsibilities The successful applicants will undertake research, under the supervision of Dr Gherardo Finocchiaro, Prof Sanjay Sharma and Prof Michael Papadakis in inherited cardiac