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Robotic Systems for Smart Manufacturing Program is developing the measurement science needed to enable manufacturers to characterize and understand the performance of robotics systems within
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This program involves multimodal imaging techniques that use magnetic resonance imaging (MRI) as either a base or as a complimentary technique. Multimodal imaging combines information from two or more imaging
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industries and research sectors. Our research group is interdisciplinary, drawing from diverse previous research experiences including wet-lab and computational work. Interested candidates are invited to reach
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simulation techniques. In addition, simulations and examination of the overall separation process may require computational studies across multiple length scales. key words Modeling; Nanotube; Molecular
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the computational determination of 3-D features of a specimen from a series of their 2-D projections. By carefully preparing the specimen, designing the experimental acquisition, and subsequent data processing, semi
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RAP opportunity at National Institute of Standards and Technology NIST MEMS-Based Scanning Probe Microscopy Location Physical Measurement Laboratory, Engineering Physics Division opportunity location 50.68.31.B7380 Gaithersburg, MD NIST only participates in the February and August...
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NRC Programs at National Institute of Standards and Technology This page provides specific information related to the NRC Research and Fellowship Program at NIST. Use the navigation on the left
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controls. The position will require programming skills, mostly with Python. To understand the vision of beamline operations inspiring this opportunity, see these two recent publications: https://doi.org
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://jarvis.nist.gov/) infrastructure uses a variety of methods such as density functional theory, graph neural networks, computer vision, classical force field, and natural language processing. We are currently
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[3]. [ 1] OpenAI Microscope, a collection of visualizations of every significant layer and neuron of 13 important vision models, URL [ 2] Peter Bajcsy et al., “AI Model Utilization Measurements