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will hold or be close to completion of a relevant first degree. You will need to undertake experimental research and collect, analyse and present data, working in collaboration with a post-doctoral
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, including molecular clouds (properties, formation, evolution), dynamics (supermassive black hole mass measurements, gas flows, active galactic nucleus feedback), and any other facets of the data not yet
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generate key structural and biophysical data to support the design of small molecule inhibitors with particular focus on protein production and crystallisation, solving protein-ligand structures, fragment
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to model immune-mediated kidney injury. Quantitative and reproducible read-outs from these assays will be correlated with human kidney ‘omics data to evaluate the impact of pathway modulation, ultimately
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DPhil students, manage data analysis pipelines, and contribute to publications and grant writing. This post is ideally suited to someone aiming to secure a long-term fellowship and build an independent
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lab has developed the OrthoFinder comparative genomic methods. OrthoFinder has become widely-used in comparative genomics research, it powers many popular databases of online genomic information, and
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becomes essential. This project will focus on building a comprehensive digital twin of a future quantum computer to investigate how classical subsystems scale and interact, and how this scaling impacts
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at command line and BASH scripting Experience working with large scale, complex datasets and data wrangling skills Strong publication record and familiarity with the existing literature and research in
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must hold, or be close to completion of a doctoral degree in a relevant field (e.g., data science, geography, environmental science, public health, economics). You will have experience relevant for food
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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data