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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
system and user behaviour. Join a growing portfolio of projects in artificial intelligence, natural language processing, and machine learning. These projects build on the School’s strengths in AI, machine
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Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skills that are relevant to industry demands
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machine learning. The successful candidate will develop and apply methods that integrate multimodal molecular and clinical data (genomic, epigenomic, transcriptomic) across serial patient timepoints
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organisational, communication, and relationship management skills. • Strong computer literacy, including Microsoft Office applications and database management systems such as REDCap. • The ability to work
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and capability gaps. Learning Objectives: Through this opportunity, you will gain knowledge and practical experience in biosurveillance, emerging biological threats, public health preparedness, program
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comparative analysis across the cities, identifying common lessons learned and policy recommendations contribute to background working papers, policy briefs and blogs, and participate in workshops and webinars
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Schemes of Service: Research Division: Infocomm Technology Employment Type: Fixed Term As a University of Applied Learning, the Singapore Institute of Technology (SIT) works closely with industry in
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-driven reusability assessment platforms integrating NDT data, machine learning models, and RFID-enabled traceability systems. Prepare and draft technical reports, conference/journal papers, and
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, quantitative characterization, and machine learning-enabled decision-making. The position is associated with the recently awarded ERC Consolidator Grant project “Engineering Multivalency for Superselective
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graphs (ARGs). Research areas include statistical/quantitative/population genetics, genealogical inference, machine learning, genetic prediction, genome-wide association studies, scalable linear mixed