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orientation. Relevant projects may make use of large-scale survey data, administrative or register-based data, repeated cross-sectional or longitudinal designs, evaluation studies, or combinations
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contribute to advancing population health outcomes and addressing some of the most significant public health challenges. In this role, you will work with large-scale linked, surveillance and administrative
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psychological attributes), as well as secondary data analyses in large scale ongoing cohorts evaluating biobehavioral mechanisms by which optimism may lead to healthier aging. Additional work will include
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digital twins at scale. The doctoral candidate will create computational approaches capable of estimating anatomical, mechanical and electrophysiological parameters from large imaging datasets while
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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following projects: Project #1 Title: Enhance Students' Interdisciplinary Learning through Large Language Model-Empowered Learning Analytics (MOE TRF 2/25 ZG) Project #2 Title: Building an Eco-system
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and single-cell omics (transcriptomics, proteomics, epigenomics, metabolomics, meta-transcriptomics, etc.) data. Independently carry out computational and bioinformatics analysis for large-scale spatial
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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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, the Research Fellow will execute defined research workstreams, contribute to analytical strategy, and generate reproducible and clinically meaningful insights from large-scale genomic and health data. Research
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security guarantees. In parallel, we will investigate how modern AI techniques can accelerate large-scale formal verification, developing AI proof agents that can maintain, extend and refactor the seL4 proof