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in meta-analysis, systematic reviews, or quantitative synthesis methods Hands-on experience with coding large datasets, developing taxonomies/ontologies, and implementing AI/ML classification pipelines
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experience in data management, analysis, and visualisation. You will demonstrate experience working with large-scale biological datasets, preferably from biobank studies (e.g., UK Biobank, China Kadoorie
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trials (e.g., diet, FMT), and ex vivo gut models enabling advanced multi-omics analyses of these samples. In addition the lab also maintains a large culture collection, partially linked to genomic data
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biology, human microbiota, microbiology or other relevant areas and/or comparable expertise (e.g from industry). Demonstrated expertise in omics data analysis, for example with (meta)genomics
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placed on synthesis approaches, such as meta-analytic techniques and the analysis of large-scale health data, to systematically integrate evidence and identify patterns across diverse health outcomes
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. The Research Assistant/Research Associate will play a key role in technical and scientific work of unifying large scale genomic (WGS, GWAS, long read) and non genomic (EHR, lifestyle, clinical) data from
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based at the School of Electronics and Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing
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Meta/Facebook, AI and Systems Position ID: Meta/Facebook -AI and Systems -RESEARCH [#29126] Position Title: Position Type: Government or industry Position Location: Menlo Park, California 94025
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(large scale heterogenous data synthesis, meta-analytic studies, conceptual synthesis) Experiences and interests in shaping modern team science research and interest in super-visioning & coordinating
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta