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We seek to recruit a Research Associate/Fellow to join our team developing a groundbreaking technique based on autofluorescence (AF) imaging and Raman spectroscopy for detection of positive lymph
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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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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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(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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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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(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
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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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-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