60 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" uni jobs at Nature Careers in United Kingdom
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. On-site secure parking All applications must be made via our website: https://www.crukscotlandinstitute.ac.uk/careers/scientific-officers.html Closing Date: January 30th (applications reviewed on a
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Full time, fixed term role based at our Hammersmith Campus. If you require any further details on the role, please contact: Dr Ben Jones - ben.jones@imperial.ac.uk Apply: https://www.imperial.ac.uk/jobs
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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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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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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
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(e.g. Nextflow) and cloud compute environments (e.g. OCI, AWS, GCP) Familiarity with Bayesian methods, machine learning, or causal inference in the context of biological data Contributions to open-source
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. Collaborate with interdisciplinary EIT Oxford teams to link fundamental cell-developmental genetics research to machine-learning models designed to augment the search for relevant target genes. Requirements
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workshop for digital and conventional manufacturing (CNC, laser machining, additive manufacturing). At Principal level, you will also contribute to management and long-term technical strategy, infrastructure
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of technology which integrates robotics, computer vision, and data infrastructure to enable scalable, reproducible and data rich transformation platforms, supporting the team with subject matter expertise