288 machine-learning "https:" "https:" "https:" "https:" "https:" "Dana Farber Cancer Institute" positions at Zintellect
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. Description: Theoretical research and computer simulation are carried out with emphasis on observations of space plasmas. Specific interest areas include (1) nonlinear phenomena in unstable collisionless
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areas. These include, but are not limited to: Applying machine learning algorithms to solve real-world problems. Creating and structuring databases for storage, retrieval, and image analysis. Determining
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, austere conditions. Learning about military deployment health and gain experience in environmental data collection. Contributing to solutions for difficult environmental health problems in complex
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Instrument for Magnetic Sounding (PIMS) on the Europa Clipper Mission. Space Sci Rev 219, 62 (2023). https://doi.org/10.1007/s11214-023-01002-9 3. Kataoka, R., Nakano, S. & Fujita, S. Machine learning emulator
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inference (otherwise known as spectral retrieval), which involves using forward models in conjuction with Bayesian or machine learning-based techniques in order to derive posteriors on parameters of interest
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project), create a unique opportunity to apply machine learning and neural network methodologies, in conjunction with simplified ice sheet models, to advance understanding of ice sheet basal processes and
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development to support extended reality technologies, machine learning pipeline integration, integration of sensors/devices to mobile platforms, and creating novel clinical decision support applications for our
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. Description: Research opportunities exist within the applied Earth sciences and applications of Earth remote sensing to employ data from the upcoming NISAR mission (https://nisar.jpl.nasa.gov/) to develop and
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improve estimation of rates of snow accumulation, snowmelt, ice melt, and sublimation from snow and ice worldwide at scales driven by topographic variability. We seek projects focusing on the use of machine
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that are facile with computationally efficient, rigorous machine learning for image region identification, demonstrate an understanding of both planetary and scalable computer science, and have publication