674 machine-learning-"https:"-"https:"-"https:"-"https:"-"RAEGE-Az" Postdoctoral positions in United States
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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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the appointment start date; demonstrate strong expertise in computational biology or data-driven modeling, with experience in one or more of the following areas: machine learning or deep learning, structural
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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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that promote current research. Prepares and publishes scientific manuscripts under the direction of the Principal Investigator. May be responsible for operation of specific equipment. May teach techniques
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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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working on and publishing refereed papers on problems related to discrete and continuous optimization and machine learning is preferred A combination of education and relevant experience from which
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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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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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simulation models used in rare-event searches, develop machine learning methods for background rejection, and place constraints on new physics using LZ datasets. In addition, the postdoc will lead the
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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