45 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"IFM"-"IFM"-"IFM" positions at Nature Careers in United Kingdom
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of our existing data infrastructure. This can include web applications, APIs, machine learning tools, and image processing pipelines. Utilise good software development practices such as CI/CD, writing
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with experts to automate diagnostic assays, leading to cost-effective, easy to use tests Work closely with AMR, Informatic and Machine Learning colleagues ensure the tests provide accurate pathogen ID
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discipline* Good knowledge and experience in at least one of the following: cancer biology / cancer immunology / epithelial cell biology / evolutionary biology / genomics / statistics / mathematics or machine
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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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testing. Experience of working with genomic data at a population scale, including the tools and technologies to manage sophisticated analyses. Experience of statistical and/or machine learning methods
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, plant transformation, plant breeding, computer vision assisted automated phenotyping, machine learning and AI. The role will require working with other institutional stakeholders to scope, design, equip
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for early-stage cancer using statistics and/or machine learning (including deep learning where appropriate). You will join a vibrant and growing research group of 12 scientists (six postdoctoral researchers
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Foundation, explores how pathogenic bacteria survive and cause infection. Working at the interface of genetics, chemical biology and machine learning, the team develops small molecules to disrupt key bacterial
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the Centre, enables collaborations in data analysis, computational modelling, machine learning and theory. SWC also benefits from interaction with the wider UCL Neuroscience community, which brings together
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of emerging methods in metabolic analysis, metabolic modelling, machine learning, and data-driven biology, identifying opportunities to apply new tools to accelerate discovery. Work closely with experimental