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commercialization of the machine learning program. Designs research protocols. Assists in developing and conducting research projects, including experiment design, data analysis and documentation of experiment
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models and data processing pipelines using knowledge of best practices with partners and various sources in machine learning and statistical inference • Provide expert-level knowledge through data
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in a clear, concise manner. Computer literacy, including the ability to use a variety of software packages to analyze data. Required Qualifications PhD or master’s degree with 4 years of experience in
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to space weathering and soil alteration and devise ways of testing it. Acquire, assemble, and perform analyses of data and interpret results. Manage research project with other SSERVI institutions and
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Postdoctoral Research Associate (Genomics, Data Integration, Deep Learning, AI) - Radiation Oncology
. Experience with Git, Docker, Common Workflow Language, and Cloud computing. Deep understanding of concepts and techniques in machine learning/deep learning with Scikit-Learn, Keras, and/or TensorFlow and
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inclusive and equitable working and learning environments for all students, staff, trainees, and faculty. We believe that building a diverse team enriches us individually and as a department by exposing us to
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background in Quantitative Statistics. Experience on RNA-seq analyses (STAR, Salmon, DESEq2, DCC) and machine learning. Working knowledge of UNIX, Perl/Python, R, and Docker. Analytical reasoning and problem
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driven to initiate and maintain the excellent operation of the TSC. Working knowledge of computer software including Microsoft Word and Excel and willingness to learn database management. Strong
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facilities at the PSC, including all video and computer equipment. Teaches at least one course per semester (often a graduate course in the clinical training program, such as Introduction to Psychological
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gathering and reporting, education technology, curriculum operations process improvement, online course listings, course and program evaluations, internal records, coordination of PhD and MD curriculum