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to preventive interventions, decision support to implement mental health interventions, longitudinal data analysis, machine learning/NLP/AI, integrative data analysis, and related grant writing. This candidate
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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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education, with an emphasis on social and emotional learning, or curriculum and instruction K-12 experience Experience teaching mindfulness programs Qualitative data collection and analysis Coordinating
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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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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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of them are required at start date. Cell culture: Culture of various cell types (cancer cells, stem cells, primary cells, organoids, neurospheres). Cell cycle analysis, 2D/3D colony formation assays, siRNA