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this suits a candidate with a background in optical systems / imaging, or with more experience in machine vision, or systems control and automation, or data interpretation. A candidate would not be expected
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developing and applying deep learning models, particularly in areas such as natural language processing (e.g. use of LLMs), computer vision (e.g. CNNs for image classification), and multimodal data integration
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experts to acquire bespoke training and testing data; develop prototype solutions informed by the latest ideas in medical imaging AI, computer vision and robotic guidance; and evaluate models in simulated
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and statistical modelling, statistical image analysis and computer vision, chemometrics, biophysics, bioengineering. Preference will be given to candidates with a demonstrated experience in applying
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We are seeking a talented, organised, and highly motivated individual to join the Department of Eye and Vision Sciences at the University of Liverpool as a Postdoctoral Research Associate, funded by
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immunohistochemistry, along with experience in fluorescent microscopy, image capture and Adobe photoshop are also essential. Main duties and responsibilities Carry out research/experiments. The main duty is to perform
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immunohistochemistry, along with experience in experience in fluorescent microscopy, image capture and Adobe photoshop are also essential. Main duties and responsibilities Carry out research/experiments. The main duty
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Engineering, Science and Theory (NESTiD) Scientific Computing (SciComp) Vision, Imaging and Visualisation (VIViD) We are ranked 4th in the UK in the Complete University Guide 2024. For more information, please visit our
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of the collaborative interdisciplinary nature of our research, which also includes strong industrial partnerships. The strategic vision for the Department includes a major growth over the next five years, with a
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across the Respiratory Science disciplines with a vision to eliminate lung disease by prevention, early diagnosis and improved treatments. We are leading the field in understanding mechanisms of disease