705 machine-learning-"https:"-"https:"-"https:"-"UCL" Postdoctoral positions in United States
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. Responsibilities: • Design and conduct experimental research on paper-based materials for food packaging applications. • Develop predictive models for packaging performance using machine learning and physicochemical
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to study chemical transformations in materials. 2. Artificial Intelligence Applications: - Leveraging conventional machine learning techniques for materials property prediction and Bayesian approaches
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-funded projects focusing on metacognitive processes and self-regulated learning across learners, STEM, and biomedical learning tasks and domains, using serious games, simulations, and immersive learning
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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driving simulators (e.g., CARLA, SUMO) or robotic simulation environments (e.g., Isaac Sim, Gazebo). Experience with ROS or ROS 2. Experience with PyTorch or other machine learning frameworks. Hands
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in areas related to quantum information theory, quantum error correction, and machine learning. The position will start in fall 2026 and will be for two years, with a possible 1-year extension pending
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National Aeronautics and Space Administration (NASA) | Huntsville, Alabama | United States | about 2 months ago
to advance use of foundation models for Earth Science research and applications, including fine-tuning experiments Use of remote sensing, models, and/or machine learning to further our understanding
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beyond the Standard Model, including effective field theories and perturbative QCD, phenomenology at current and future colliders, as well as emerging areas in Artificial Intelligence, Machine Learning
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county across the state, Texas A&M AgriLife is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about
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-principles and atomistic simulations with machine-learned interatomic potentials to: Model reaction pathways on metal-oxide surface, including adsorption, reactions and diffusion steps. Construct atomistic