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, The Open University or the University of Oxford. Fellowships are aimed at those in the immediately postdoctoral stage of their career, to provide the opportunity to consolidate their PhD through developing
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research building on previous research from the Silvi research group. The research will focus on the development of novel reactions based on photoredox-radical chemistry. The new radical processes will be
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requirements. The role contributes to the development of personalised interventions informed by both clinical expertise and lived experience. The position is responsible for supporting the coordination and
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Center for Biologics Evaluation and Research (CBER) | Southern Md Facility, Maryland | United States | 3 days ago
various stages of parasite development. This is a pioneering project that seeks to define the global post-transcriptional regulatory processes involved in parasite transmission. The results of these studies
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and validate a prototype microscope with enhanced sensitivity and contrast, evaluate label‑free and fluorescence imaging modes, and develop robust hardware/software workflows for low‑light measurements
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Propose and develop state-of-the-art methods to overcome existing problems. Train and evaluate the models and systemically compare them with recent models. Prepare papers and reports. Develop systems based
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the UK) and work within the Department of Cancer and Genomic Sciences. Their main role will be to apply and develop new methods for precise genome manipulation to create transgenic reporter models
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As Computational Senior Research Fellow, you will: · Develop cutting-edge methods to predict which somatic mutations drive clonal expansion · Integrate large-scale multimodal omic datasets from healthy
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, including deep neural networks and physics-informed neural networks, to analyse large datasets from gyrokinetic and fluid simulations of plasma turbulence Develop and train reduced-order models that capture
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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during