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We are seeking a full-time Postdoctoral Research Assistant to join a cross disciplinary research project to improve our understanding of colorectal cancer. Deep learning has revolutionised image
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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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processing in the mammalian cochlea in vivo , and how these influence central auditory neuronal pathways. The project will primarily involve using in vivo 2-photon imaging and AAV-gene delivery applied to a
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will involve processing medical image data sets, taking experimental measurements of movement patterns in living birds and conducting dynamic physics simulations of locomotion in both living birds and
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exploring B cell migration in the spleen. The successful candidate will have extensive experience in handling and processing live spleens and an established expertise in live imaging of this organ. As a
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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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(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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electrophysiological recording (cMOS Multielectrode array) and calcium imaging and optogenetics. The long-term goal is to understand how cortical organoids process information. For this aspect there will be
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(model performance) and quality studies (human factors assessment). You should hold a relevant PhD/DPhil (or be near completion) in computer vision or robotics or biomedical image analysis. Knowledge
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classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high-throughput, automated analysis of large image datasets from fluorescence