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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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the necessary data. They will also assist with training and data collection involving the geo-QDM in non-MMESSH related projects. They will write and publish any relevant scientific research papers on geo-QDM and
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at the intersection of these research areas. You should hold, or be close to completing, a PhD/DPhil in mathematics, statistics, physics, engineering, data science, or a related field. Experience in cancer
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recruitment and clinical assessment activities and helping design the neurofeedback protocol, including monitoring data quality for clinical interviews, data collection of online databases of self-report
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data independently. The post holder must also have a strong statistical background, with at least one recent publication in an internationally reputable journal. Application Process You will be required
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understand the links between maturational accounts of acquisition and the stability of morphosyntactic properties diachronically, with particular reference to French. Acquisitional data will be extracted from
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in vivo experiment documentation, sample inventories, and reagent information. You will also be responsible for ensuring that work in the laboratory is conducted safely and that work is undertaken
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samples. The postholder will use datasets from a large NIH-funded project based in the US to develop methods for jointly analysing dMRI and PS-OCT data. The post holder will be expected to take an
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responsible for managing your own academic research and administrative activities and adapting existing and developing new research methodologies and materials. You will analyse quantitative data from a variety
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necessary for the benefit of the project; • employ simulations and data analysis routines to analyze your data; • help to establish a scientifically outstanding and warmly communicative