20 assistant-professor-computer-science-and-data-"St"-"St" Fellowship positions at University of Texas at Austin
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of nuclear and radiation engineering, including imaging, robotics, high-performance computing, reactor design and materials development. The groups provide a supportive community striving to solve the most
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computational cluster and/or high-performance computer with Linux OS. Excellent oral and written communication skills and a collaborative working mindset. Strong publication record in closely related research
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and are also involved in planetary missions and climate modeling. These research projects produce large data sets and require computational analysis and visualization. This position is for one year with
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, Education and Communication/Meaning-Making and (2) Environmental Data Science and Spatial Computing. This job listing is for area one, which focuses on the integration of social science research with
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open science. Prepare and lead manuscripts for publication related to study aims. Mentor Ph.D. students and/or research assistants in data analysis, manuscript preparation, and other research activities
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researcher with a strong background in hydrology, numerical/analytical modeling, programming, and data management/analytics is needed to support existing and forthcoming projects in the BEG hydro group
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resources and systems in service to our mission as the state geological survey. The current research program objectives are: (a) synthesize a full suite of Texas water resource and system data into a common
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resources and systems in service to our mission as the state geological survey. The current research program objectives are: (a) synthesize a full suite of Texas water resource and system data into a common
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. Experience with the use of electronic medical records and clinical data. Self-direction, critical thinking skills, and ability to work as part of a multidisciplinary team. High computer proficiency, including
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, translational science, and patient-centered care, contributing to pioneering efforts in integrating multi-modal data for individualized cancer therapy selection. The lab leads multi-institutional projects