317 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" "St" Postdoctoral positions in United Kingdom
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carried out in collaboration with Prof. Susan Lea FRS FMedSci at St. Jude Children’s Research Hospital, and the successful candidates will have the opportunity to spend periods at St. Jude in Memphis, USA
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fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train. Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC
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, Engineering, or a closely related discipline. You will be a materials or physical scientist with a strong track record in applying deep learning to computer vision problems, ideally within battery
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that integrate multi-omics data to uncover mechanisms of disease, cellular resilience, and therapeutic response. The post holder will lead research applying large-scale machine learning and foundation models
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modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
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. The project integrates synthetic organic chemistry, kinetic analysis, automation, and machine learning to establish next-generation mechanistic workflows for asymmetric organocatalysis. The project advances
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, and machine learning. The environment at GBI will allow researchers to undertake ambitious, long-term, collaborative research, and we will actively support the translation of research to commercial
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Music Centre and St Paul’s Cathedral. About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the previously unthinkable. Throughout our history, we’ve
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sophisticated machine learning tools for image processing Experience in mathematical modelling Knowledge in comparative neuroscience (comparative vertebrate neuro) Proficiency in basic computer packages (eg
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About the role – In this post, you will join a collaborative BBSRC-funded project focused on using metabolomics and machine learning to predict lameness outcomes in dairy cows. A typical day may