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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 12 hours ago
. The successful candidate will work on designing and training advanced deep learning models (e.g., U-Net, Vision Transformers) using multi-modal EO datasets (optical, radar, thermal, LiDAR) to generate high
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algorithms for the next generation of particle physics experiments and also explores other ways AI can accelerate scientific discovery. The group collaborates closely with computer scientists, astrophysicists
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machine-learning based approaches for extremely precise radial velocity studies and exoplanet spectroscopy with the NEID, HPF, and MARVEL facilities, and pursue related exoplanet and stellar science. We
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clinical data to better characterize disease processes. ● Clinical and multi-omic data fusion: Build machine learning pipelines that integrate electronic medical record data, genomics (animal and microbial
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, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis
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for soft materials, with particular emphasis on thermo–visco–hyperelastic behavior, integrating continuum mechanics, scientific machine learning (SciML), and computational physics. The project aims
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science methodologies (e.g., machine learning). Experience working with large-scale environmental and remotely-sensed datasets, strong proficiency in R and version control tools (such as GitHub), and
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Associate with background on AI and machine learning for wireless networking and communications. The successful candidate will work under the direction of Dr. Marwan Krunz, Director of the Wireless
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., machine learning). Experience working with large-scale environmental and remotely-sensed datasets, strong proficiency in R and version control tools (such as GitHub), and familiarity with high-performance