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candidates whose expertise falls within one or more of the following areas: computational and mathematical modeling, statistical modeling, machine learning, network science, bioinformatics, applied mathematics
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the use of machine learning and AI approaches • Integration of proteomics with genetic data via MR, coloc and FUSION to identify causal and druggable targets Requirements • The successful applicant will
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to contribute to one or more projects, learning advanced cellular and molecular biology and anaerobic microbiology techniques. The candidate’s day will be split between benchwork to generate data, and computer
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, ventriculomegaly). Experience in machine learning statistical methods. Experience in the acquisition of infant neuroimaging data. Prior experience working with infants and children in a research setting. Enthusiasm
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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
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applications, and ability to quickly learn and master various computer programs. Must be technically rigorous, organized, and have demonstrated excellence, innovation, and productivity in research. Ability
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., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) may be involved. Training will be provided in all methodologies but prior experience with some
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modern machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks to minutes
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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant
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experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in particular simulation-based inference), strong programming