16 algorithm-development "Multiple" Fellowship positions at University of Texas at Austin
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with rare, resistant, or genomically un-targetable cancers. Responsibilities Design and evaluate algorithms for treatment and response matching using integrated clinical and molecular datasets. Develop
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has also been developing physics-based machine learning algorithms for three dimensional seismic modeling, imaging and inversion using high performance computation including parallelization on GPUs
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and its post-graduate programs train mental health professionals to become 21st-century leaders in the fields of psychiatry and psychology. Clinical services provide informed, evidence-based care for
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Intelligence on civil society. Responsibilities Collaborate with ASI Director to build a Steering Committee of scholars and other experts as thought partners and validators in developing the metrics and
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development, IRB submission, informed consent, data collection and analysis, abstract submission, data presentation, and manuscript preparation as part of a multidisciplinary team. Perform other related duties
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using high spatial resolution satellite imagery Exceptional written communication abilities Strong programming experience Familiarity with machine learning algorithms (e.g., DCNN, XGBoost, SVM, Neural
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produce limited insight into the micron- and submicron-scale context within which the fate of sedimentary organic carbon is ultimately determined. The central aim of this project is to develop Single
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academic institutions. Our mission is to develop and execute a state-of-the-art 3D Heterogenous Integration manufacturing technology roadmap, and to provide critical pilot manufacturing capabilities
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and scholarship. The fellow will participate in writing working groups, develop an independent research project, and present a research poster at a national meeting. To ensure a well-rounded experience
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Medical School is focused on the development of new models for healthcare delivery, promotion of health, and innovation. The Department of Ophthalmology and the MSWEI seek to recruit dynamic, academically