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Field
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-quality teaching. The Hub for Applied Bioinformatics (HAB) is the Faculty’s focal point for computational biology, delivering bespoke bioinformatics support and training across genomics, transcriptomics
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-quality teaching. The Hub for Applied Bioinformatics (HAB) is the Faculty’s focal point for computational biology, delivering bespoke bioinformatics support and training across genomics, transcriptomics
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or bioinformatics and a keen interest in cancer biology. Extensive and demonstrated experience in scRNA-seq, spatial transcriptomics, with image analysis experience an advantage. A proven publication
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institutions, and leading industry partners. The successful candidate will contribute to the delivery of high-impact research projects involving AI algorithm evaluation and image data analysis. You will play a
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Technological University, NTU, and National University of Singapore, NUS, and hosted by NTU. IDMxS is focused on development of core science to drive a paradigm shift in molecular detection and analysis to link
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to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you will get unprecedented medical, biological, and methodological
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the supervision of Prof Amedeo Chiribiri within the Department of Cardiovascular Imaging, School of Biomedical Engineering & Imaging Sciences, King’s College London. About The Role Applicants should be medically
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description can be changed at any time. Required Education Doctoral Degree in Computer Science or a similar field. Required Experience In-depth knowledge of medical imaging procedures, techniques, and data
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blot, immunofluorescence) Cell transfection Mechanical testing and biomaterials characterization Quantitative image analysis Computational skills including data processing and statistical analysis Prior
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dedicated to technology such as state-of-the-art light microscopy, nano- and micro- fabrication, and computing. We are seeking a highly motivated Research Fellow with strong expertise in electron microscopy