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Field
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learning, large-scale model optimization, and generalization. To explore scalable optimization methods for large-scale, distributed, and multi-node collaborative training. To conduct theoretical analysis
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integration framework with Foundational Multi-Scale Data Processing, Temporal Relationship Intelligence, and Intelligent LLM-Powered Social Simulation and Decision Support capabilities, that leads
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, and AI-driven computational biology. The successful candidate will develop and apply innovative computational methods to analyse large-scale multi-omic datasets, identify mutational patterns across
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impact on rock / cementitious materials using established constitutive models (e.g. K&C, RHT). • Meso-scale modelling of multi-phases cementitious materials (e.g. aggregates, mortar, interfaces
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multidisciplinary initiative focused on improving maternal and infant health through advanced biomarkers, multi-omics, and translational research. This is a 6 months full time position with a competitive salary
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About the Role We are looking for a Senior Computational Biologist to join the ERC-Synergy funded MUTAHOME project, a multi-institutional collaboration between Queen Mary University of London and
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artificial intelligence and machine learning. The postdoctoral fellow will contribute to the development of a comprehensive, multi-modal framework for predicting and managing cardiovascular disease by
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an experienced, motivated quantitative Research Training Fellow to lead data integrative analyses in large-scale epidemiological studies of cancer and apply innovative methods in epidemiology, statistics and data
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single-cell genomics, multi-omics integration, or statistical genetics Familiarity with large-scale human datasets (e.g., All of Us, UK Biobank, dbGaP) Experience in autoimmune, immunometabolic
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, human-in-the-loop AI agents to aid in routine urban operations and emergency response (e.g., severe flooding, power outages, and critical water main failures). Responsibilities* Agentic AI & Multi-Agent