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develop advanced nano-imaging and nano-sensing technologies, enhanced by AI analysis, to study viral infection in real time at the cellular and nanoscale level. This interdisciplinary research combines
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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efficient for medicine? If the answer is yes, please continue reading! Join our team! We are looking for a PhD student to work on the topic of shape analysis for medical imaging, tailored for deep learning
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to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device (SFDI) and also from our custom-built photoplethysmography (PPG) sensor
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Number of awards: 2 Start dates: January, April or October 2026 About the project Two PhD studentships are available as part of the project ‘BIRES: Building body image resilience in populations
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We are seeking a research assistant with a background in computing to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device
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AI design of ultrathin lenses for compact imaging devices The Faculty of Engineering at the University of Nottingham is seeking an enthusiastic, self-motivated student who enjoys working as part of
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AI design of ultrathin lenses for compact imaging devices The Faculty of Engineering at the University of Nottingham is seeking an enthusiastic, self-motivated student who enjoys working as part of
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focus on research and development in Atmospheric Pressure Matrix-Assisted Laser Desorption/Ionization (AP-MALDI) mass spectrometry and molecular imaging, using high-resolution Orbitrap MS instrumentation
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enhanced MRI with computer simulations of image contrast and mass spectrometric imaging of tissue samples and single cells. This project is part of the Collaborative Research Centre 1450 “Insight