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. This position offers a unique opportunity to contribute to high-impact, interdisciplinary research at the intersection of network science, global research competitiveness, and generative AI capabilities
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disease (CAD). You will apply expertise in data science, machine learning, as well as multi-omics integration to predict and validate functional regulatory networks in vascular cell types. This work will
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of national research infrastructures. The ideal candidates will have a PhD in a discipline closely related to computational social science by date of appointment (e.g. network science, computer science, data
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., mindfulness-based interventions; psychedelics; music; suvorexant). Candidates must have a Ph.D., M.D., or equivalent degree prior to start date. Independent EEG processing and analysis skills are required
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well as testing effectiveness of implementation supports (e.g., professional learning communities and coaching). Responsibilities: Coordinating data collection and analysis activities. Developing and disseminating
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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
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, appointment may be renewed for an additional year contingent upon available funding and satisfactory performance The focus of this research project is to conduct multi-modal tissue analysis and perform gene
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The Biocomplexity Institute (BI) at the University of Virginia integrates scientific research – from genetic sequencing to policy analysis – to tackle the complex task of understanding massively
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scientist with a strong background in single-cell genomic data biology and analysis to contribute to this important work. The successful candidate will work on uncovering pathways that drive susceptibility
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cell biology. Additional preferred qualifications include: experience in mouse models of disease, flow cytometry, single-cell RNA sequencing analysis, and image processing and analysis. Qualified