21 algorithm-development-"Prof"-"Washington-University-in-St" Postdoctoral positions at King's College London
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of Biomedical Engineering and Imaging Sciences is a cutting-edge research and teaching School dedicated to development, translation and clinical application within medical imaging and computational modelling
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team with access to both cutting-edge computer power and advanced image acquisition capabilities. Together, we are developing next-generation Ai-assisted robots for colorectal cancer treatment. About The
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Trust collaborative Award led by Prof. Oscar Marin and Prof. Beatriz Rico. As a member of the team, the post-holder will be working in close collaboration with a multidisciplinary team of researchers in
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Moutoussis (UCL), Co-I Amir Englund (KCL), Co-I Syeda Tahir (City) and PI Prof Mitul Mehta (KCL). This role will provide you with the freedom to develop new and creative ideas, methodologies, and/or approaches
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
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the formation of the brain during embryonic development and in early postnatal life. This is based on the understanding that early experience shapes the way our brain is constructed. While the “ground plan
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6. Desirable criteria Evidence of active collaboration with dry lab and co-development of algorithm for the prediction of epitopes. Downloading a copy of our Job Description Full details of the role
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-disciplinary research environment Desirable criteria 1. Experience in devising and developing novel machine learning algorithms 2. Hands on experience with ROS and physical robots 3. Excellent
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on mentoring and career development. The School hosts the British Heart Foundation Centre of Research Excellence at King's, which brings together a unique range of internationally recognised scientists
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dedicated to development, translation and clinical application within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical