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research for understanding the learned algorithms in brains and machines. The post holder will provide guidance to less experienced members of the research group, including postdocs, research assistants
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responsibility for carrying out research for understanding the learned algorithms in brains and machines. The post holder will provide guidance to less experienced members of the research group, including postdocs
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and apply state-of-the-art modelling, characterisation, and machine learning techniques to understand how batteries behave and age. Collaborating with project partners, you’ll turn these insights
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, ensuring they are kept fully up to date with progress and difficulties in the research projects. It is essential that you hold a PhD/DPhil in a quantitative discipline (e.g. Statistics, Machine Learning
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properties of Li-rich three-dimensional materials for lithium battery cathodes using density functional theory (DFT), molecular dynamics, cluster expansion, machine learning computational techniques. This work
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Oxford’s Department of Orthopaedics (NDORMS) as well as collaborators in Bristol and Cardiff. You should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely
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-contact manipulation/locomotion, machine learning and optimisation, avatar animation or related areas. You have experience working on real robots and great team working skills. Informal enquiries may be
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capabilities o Demonstrated experience with machine learning and/or statistical modeling o Expertise in handling large-scale, complex datasets with strong data wrangling skills o Strong publication record
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experience in machine learning and image analysis for ultrasound images and video. The successful applicant will possess specialist experience conducting fieldwork, particularly in low-resource or rural
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The post holder will develop computational models of learning processes in cortical networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity