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Description of the offer : To further advance and refine novel techniques—such as x ray detected ferromagnetic resonance (XFMR) with polarized soft x rays in reflectivity and diffraction geometries
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project investigating mechanosensing in Diptera. This post will focus on using detailed wing geometry models and kinematic measurements in computational fluid and structural dynamics simulations to recover
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We are seeking a full-time Postdoctoral Research Assistant in Machine Learning to join the Visual Geometry Group (VGG), in central Oxford. The post is funded by Toyota Motor Europe and is fixed-term
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statistical shape analysis, Riemannian geometry, time series and stochastic processes, and Bayesian statistics. Key responsibilities: To carry out research within the framework of the project, under
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statistical shape analysis, Riemannian geometry, time series and stochastic processes, and Bayesian statistics. Key responsibilities: To carry out research within the framework of the project, under
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and PhD students. Research spans a wide range. Current interests include: Bayesian statistics; modelling of structure, geometry, and shape; statistical machine learning; computational statistics; high
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computational sciences, decision-maker education campaigns, and training the next generations of technology governance leaders. It is one of the few organisations in the world to focus on the governance of AI
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these bioinformatic experiments. Access to a high-performance computer will be provided. The candidate must be capable of generating complex molecular compound models in silico and using current molecular dynamic
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The Adaptive and Intelligent Robotics Lab (AIRL) in the Department of Computing at Imperial College London is seeking a talented Research Associate (post-doc) or Research Assistant (pre-doc) to work
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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