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within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical questions and is aimed at novelty, understanding of physiology and
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within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical questions and is aimed at novelty, understanding of physiology and
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for conditions such as otosclerosis. The position requires expertise in medical image analysis, proficiency with neural network architectures (particularly CNNs for segmentation tasks), and experience processing
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(imaging mass cytometry); maintenance of tissue archives and databases. Key attributes of the successful applicant include: PhD in a relevant subject Substantial experience of histological techniques
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Centre in the Denmark Hill Campus. The applicant should have a PhD in Biomedical Engineering, Medical Physics, Medical Imaging, or a related area (or pending results). They should have good analytical and
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process. Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made
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Centre in the Denmark Hill Campus. The applicant should have a PhD in Biomedical Engineering, Medical Physics, Medical Imaging, or a related area (or pending results). They should have good analytical and
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Centre in the Denmark Hill Campus. The applicant should have a PhD in Biomedical Engineering, Medical Physics, Medical Imaging, or a related area (or pending results). They should have good analytical and
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quantitative proteomics and imaging to understand microtubules impact cell physiology. We offer a vibrant and creative multidisciplinary environment, and the successful candidate will be supported to develop
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal