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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
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analysis of complex biological data, including big omics data. Dr. Floyd is Co-Director of the UW Cardiovascular Health Research Unit (CHRU) (https://chru.washington.edu/ ), where he leads a broad portfolio
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. The position requires experience with at least one of the following: Data Science, Machine Learning, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management
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Science, Computational Biology, Bioinformatics, or a closely related field. Experience with computational modeling, statistical programming (Python, R, MATLAB), or machine learning. Experience working with large datasets
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mental health supports consistent with Loyola University Chicago’s commitment to advancing social justice through transformative education. Computer Skills Skilled in using various software programs (e.g
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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vision and novel applications of machine learning. Advanced knowledge of R or Python is required. Intermediate knowledge in C/C++ and/or at least one SQL dialect is preferred. Apply online at https