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experimental approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory
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computing and/or cloud computing; familiarity with Earth system models through model development, model execution, and/or model performance diagnoses; applied mathematics methods such as machine learning
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, researchers, and students on foundational machine learning and biologically informed scientific applications. The position is particularly well-suited to candidates eager to apply their technical expertise in
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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analysis, GIS, and large environmental datasets Experience developing predictive or machine learning models for environmental systems Demonstrated record of peer-reviewed publications Experience
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machine learning models. Working with extremely large, multi-modal datasets. Prior experience in analysis of clinical health records, and time series data are highly preferred. Qualifications Requirements
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Integrate multi-omics data with clinical, cognitive, and imaging phenotypes in longitudinal cohorts Develop and apply statistical and machine-learning models (e.g., mixed-effects models, survival analysis
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modalities. The candidate will have the opportunity to work on “big data” studies in health and diseases, including Alzheimer’s disease and others such as schizophrenia, psychosis, autism, etc. We collaborate
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applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA
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Genome Curation Assistant (Jarvis ): an AI system that combines modern machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting