2,704 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in United States
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develop and apply computational approaches for mass spectrometry data, with artificial intelligence/machine learning (AI/ML) being a major focus. They will have an opportunity to lead and contribute to a
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 4 hours ago
Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal
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appointment) in a relevant field, such as geodynamics, seismology, geophysics, or computational science. Ability to sit for extended periods (e.g., 6–8 hours/day). Visual acuity to read computer screens and
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relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and
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Required Knowledge, Skills, and/or Abilities Demonstrated experience in computation/computer algorithms, as evidenced by relevant publications. Desirable Knowledge, Skills, and/or Abilities Proficiency in
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mechanics, molecular dynamics, and data science tools to study the mechanisms of catalytic organic reactions and enzymatic reactions. Job Qualifications: · PhD in Computational Chemistry, Organic
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sciences, computer science, machine learning, and education research. Research Themes The research themes identified for the NTO postdoc include, but are not limited to, the following: Developing
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. The EIC will be a discovery machine for unlocking the secrets of the “glue” that binds the building blocks of visible matter in the universe. The machine design is based on the existing and highly optimized
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, Aurora) and one of the brightest synchrotron x-ray sources in the world (APS). Candidates with a background in deep learning, computational physics, computational materials science, inverse problems
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genome-based influenza and/or SARS-CoV-2 risk assessment algorithms, designing broadly protective influenza and SARS-CoV-2 vaccines using Machine Learning and Artificial Intelligence, modeling the impacts