1,660 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "IFM" "IFM" "IFM" positions at Nature Careers
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integrating clinical big data and artificial intelligence, the program aims to identify novel therapeutic targets and accelerate the translation of fundamental discoveries into personalized medical solutions
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through collaborative participation, supporting team operations and contributing to internal service roles. These are research focused positions. Further information can be found by viewing UQ’s Criteria
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, while being supported in developing programming and data analysis skills. Projects may include generating and analysing complex genomic datasets, contributing to method optimisation, and engaging in
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Fabrication (DigiFAB) and the UKRI-funded AI for Chemistry Hub. Together, these initiatives provide exceptional opportunities for high-throughput chemical biology and medicinal chemistry, drug discovery, data
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quality. Your main tasks will consist of: Conducting independent research of high international quality, including publication in leading journals Performing advanced spatial data analysis, including
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has a focus on high quality research in the fields of health data science, trials and behavioural interventions delivered in the community, out-of-hours and emergency services and other health care
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believe that better imaging leads to better outcomes for every patient. Job Summary Operates under general supervision. Performs data abstraction, collection, and entry to support clinical research
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About the Role: Advanced imaging techniques at HHMI Janelia Research Campus produce large amounts of image data that require at-scale analysis to extract useful information about the biological
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panel, shape the selection of strategic alternatives. Most of his research uses large-scale quantitative data analysis alongside semi-structured interviews. He uses unique data that he collects by
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computational methods and algorithms for genomic sequencing data analysis, particularly in the context of genome assembly. This is an exciting opportunity to develop novel computational approaches for microbial