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, health service and applied research, meta-analysis and evidence synthesis, prevention and early intervention, psychological approaches, psychopathology/ phenomenology, neuroimaging, real world data
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decentralised algorithms, meta-information data structures and indexing techniques to enable large-scale data search across Personal Online Datastores (pods) hosted on distributed pod servers, addressing both
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Hiroshima University, The International Institute for Sustainability with Knotted Chiral Meta Matter Position ID: Hiroshima University -The International Institute for Sustainability with Knotted
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Position Number: 0053061 Location: Parkville Role type: Full-time; Fixed Term until June 2027 Faculty: Science School: Physics Salary: Level A - $85,555 - $116,094 p.a plus 17% super (PhD entry
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of aligned experience, with the last 2-3 three years in the AI/ML arena, is required for consideration An overall combination of equivalent experience may be considered. Masters and PhD degree holders in
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Your Job: You will work on the development of (meta)data extraction tools from electronic lab notebooks You will create tools for data transformation and integration from electronic lab notebooks
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and energetic constrains ammonia-oxidizing and methane-oxidizing microbes exhibit under oxygen depletion (and while producing oxygen) including single cell activity assays. Applicants should have a PhD
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-00368-1 ) or executing large-scale meta-analyses of mass spectrometric datasets (https://www.nature.com/articles/s41592-021-01194-4 ). The research is computational in nature but involves close
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | 30 days ago
of questionnaires and interviews. Assist with grant writing, including drafting proposals, identifying funding opportunities, and supporting the submission process. Lead or contribute to systematic reviews and meta
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)—topics including, but not limited to: · Physics-informed neural networks (PINN) & neural operators · Physics-aware convolutional neural networks (PARC) · Meta-learning/transfer