36 computational-geometry-phd Postdoctoral positions at KINGS COLLEGE LONDON in United Kingdom
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opportunities to develop research profile, to travel to give demos and presentations, and to write academic papers. Candidates should hold a PhD degree in Computer Science or other relevant discipline. The
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experience: Essential criteria PhD in computer science, AI or related area Proven experience of knowledge engineering and semantic technologies such as knowledge graphs, ontologies, data modelling, RDF, RDFS
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and experience: Essential criteria PhD in bioinformatics, computational biology, or a related discipline * Extensive experience and expertise in analysing/ training models on biological or chemical
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About us A post-doctoral research associate position is available at the Photonics & Nanotechnology group, Physics Department, King’s College London, funded by the EPSRC Programme Grant New
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-term persistent infections, as well as on newly emerging variants of concern (VOCs). We are seeking a highly motivated candidate that has obtained (or will obtain soon) a PhD in a relevant subject (i.e
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work closely with Prof. Hanna Kienzler, Prof. Stephani Hatch, and Dr Rebecca Rhead as part of the Centre’s ‘Marginalised Communities’ programme. This involves partnering with Black and racially
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of York. Based within the Addictions Department at the Institute of Psychiatry, Psychology & Neuroscience (IoPPN), the post-holder will support a five-year programme of research across three priority areas
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programme investigating early lung fibrosis at King’s College London. It is anticipated candidates will have a relevant PhD in immunology or respiratory sciences, and have experience with cell culture
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you To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD degree (completed or in the final stages) in sociology, social policy
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models