703 machine-learning-"https:"-"https:"-"https:"-"UCL" Postdoctoral positions in United States
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contribute to the Lab’s broader effort in conversion and separation of carbon-based materials. The role will require the individual to work with personnel that perform machine learning and molecular
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Professor Fei Lu and Bloomberg Distinguished Professor Mauro Maggioni on topics including mathematical foundations of data science and statistical/machine learning, with an emphasis on inverse problems and in
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | about 12 hours ago
atmospheric science, physics, and computer science. Experience in analyzing satellite data and geospatial data are desired. Skills in AI, machine learning, deep learning, keras, Pytorch are preferred. A proven
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Geosciences, Environmental sciences, Civil or Environmental Engineering, Physics or Mathematics or a related discipline Experience in programming (e.g., Python, MATLAB, or similar), interest in machine learning
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). Duke is committed to encouraging and sustaining work and learning environments that are free from harassment and prohibited discrimination. Duke prohibits discrimination and harassment in
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astrophysics, exotic core-collapse supernovae, and machine learning methods for time series analysis. A PhD in Physics, Astronomy, or a closely related field is required. The position will entail work on a
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Computer Engineering or a closely related field, completed by the start date of the appointment. U.S. citizenship is required. Research experience in at least one of the following areas: ● Chip design
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to 8 hours/day, lifting <30 lbs, may view computer up to 8 hours/day. Shift Generally Monday – Friday, 8a-5p with some flexibility Job Summary This position is designed to have 70% clinical time, 15
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, machine learning, and control in the energy sector. The postdoc researcher will perform theoretical study and algorithm development on optimization/control/data analytics methods and authorize peer-reviewed
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. The Luque lab has recently developed an interdisciplinary approach that integrates theoretical biophysics, molecular dynamics, machine learning, and bioinformatics to predict the capsid structures