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-on experience in at least one of single-cell or spatial omics, imaging, or other high-dimensional biological data types. You interrogate existing literature critically, design rigorous experiments and deliver
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applicant has experience in cardiovascular research and/or working with the zebrafish model. Experience of hPSC-derived in vitro models, 2D and/or 3D and experience in imaging, including use of confocal and
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paradigm shift in our fundamental understanding of the charge-lattice interactions that govern such effects. This PDRA role will utilize several state-of-the-art spectroscopic techniques, such as transient
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management of group research computing infrastructures. About You You should be educated to degree level (preferably in biomedical/image analysis/medical research or an allied field) or have equivalent
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cancer biology, molecular biology, medicine, or neuroscience. Your expertise will include molecular biology techniques, advanced microscopy, quantitative image analysis, and primary tissue culture, all
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integrated THz technologies for THz time-domain spectroscopy and imaging. The project will unite novel nanowire THz technology from Oxford with unique custom-designed integrated circuits from project partners
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the laboratories of Prof Carlo Rinaldi. You will be well supported by the existing joint team, currently consisting of postdocs, bioinformaticians, PhD candidates, and research assistants. The post holder will be
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an interdisciplinary team working to build this new paradigm of environmentally-friendly and functional living materials, which can be used in diverse application domains. The project team includes leaders in
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will employ methods such as electrophysiology, biological imaging, histology, laser vibrometry and behavioural assays to study the gustatory system in bees and vibration sensing in a variety of insects
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exciting recent technological advances, having its impact on fields as diverse as medical imaging, conversational agents, astronomy, and many more. The Oxford Applied and Theoretical Machine Learning (OATML