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postdoctoral researcher for pioneering research at the convergence of artificial intelligence (AI) and materials science. The ideal candidate should possess expertise in scientific machine learning (SciML
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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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maintain statistical / machine-learning pipelines (Python / R; Snakemake/Nextflow) for long-read transcriptome assembly, sQTL fine-mapping (SuSiE, Leafcutter) and proteogenomic isoform detection
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design. Teach a course in virtual and augmented reality with applications to civil and environmental engineering. Incorporate real-world scenarios into the course, allowing students to use state-of-the-art
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Technology and expand Center-related research lines in: · Use of machine learning methods for risk prediction, control, classification, and treatment progression · Metabolic and behavioral computer simulations
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data issues. Utilizing machine learning techniques as appropriate for data analysis. Developing computing programs and software to support research initiatives. Applying new methodologies to real-world