281 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "University of St" "St" "St" 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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. 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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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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, active geologic processes, vegetation traits, and algal biomass using hyperspectral imagery in the visible and shortwave infrared and multi- or hyperspectral imagery in the thermal IR (https
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capbilities include testing on high altitude balloons. For the past year we have hosted the award-winning Stanford-Brown iGEM team, whose wikis will give additional background in lab activities. http://2011