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Experience in statistical or scientific programming (ideally R and/or Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models and/or climate
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The post-holder will join a team of investigators working on the NERC-funded Pushing the Frontiers grant ‘Influence of complex source and environmental source conditions on eruptive plume height
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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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is concerned with the challenging problem of modeling the complex modern radio environment, where a diverse set of devices and agents share the available spectrum. In this environment, it is crucial
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Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models and/or climate predictions Ability to work in a team Ability to communicate orally in
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/statistical modelling, numerical/data analysis, and programming using Python or other languages a motivation to engage in systems approaches to understand complex risks Contact details for advert Contact Name
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analysis techniques, including space syntax, isovist measures, and visual complexity assessments. The successful candidate will work closely with researchers at Cambridge and ETH Zurich to quantify spatial
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to analyse datasets Experience in statistical or scientific programming (ideally R and/or Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models
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projects. Strong proficiency in the following areas, and the ability to integrate them into complex workflows: Computational design and 3D/4D modeling (e.g., Rhino/Grasshopper, Phyton, Unity, Blender). Point
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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