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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
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Responsibilities To undertake training to run CONDOR simulations and in biophysics as required. To run and analyse CONDOR simulations on our high-performance computing cluster. To develop a deep-learning model for
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to enable robust robot autonomy in complex, real-world environments. The post sits within our EPSRC Programme Grant in Embodied Intelligence and will advance the state of the art in localisation and scene
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with members of the team and other researchers in the Future of Food programme at the Oxford Martin School. You must hold or be close to the completion of a doctoral degree in a relevant field (e.g. data
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The Role The postholder will contribute to the UKRI-funded Great British Chemicals (GBC) Hub, a seven-year national programme bringing together 10 UK universities, industry partners, and
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researchers in the Future of Food programme at the Oxford Martin School. You must hold or be close to the completion of a doctoral degree in a relevant field (e.g. data science, industrial ecology, geography
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blades. In this role you will: Design, build and optimise optical and inductive thermal NDE rigs for curved, metre‑scale blade sections; Develop and validate forward–inverse heat‑transfer models
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. The successful applicant will investigate the structure, dynamics, and motility of the bacterial Type IV pilus (T4P) machinery in the model organism Thermus thermophilus using theoretical modelling, simulation
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to reconstruct the tree-of-life on Earth, it allows us to reveal how biological function has evolved and is distributed on this tree, and it is the foundation that enables us to use model organisms
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develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung infection. As part of this work, the postholder will