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Field
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. Experience or skills for developing optical/imaging systems. Experience or skills for system automation. Experience or skills for experimental data/image acquisition and processing in relevant coding languages
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into fabrics with different structure of performance testing and purchase different materials for prototype development; and (c) perform any other duties as assigned by the project leader, Dean of School
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Classification Code: FP01 Basic Function and Responsibility A Postdoctoral Scholar position is available in the laboratory of Dr. Budd A. Tucker in the University of Iowa Institute for Vision Research (IVR
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Support”, through analysis of MR Diffusion Weighted Imaging and MR Spectroscopy and leading other members of the wider team Specific aims of the project To prospectively evaluate non-invasive pre-surgical
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 6 hours ago
Organization National Aeronautics and Space Administration (NASA) Reference Code 0147-NPP-NOV25-ARC-EarthSci How to Apply All applications must be submitted in Zintellect Please visit the NASA
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(33%) • Conduct full stack development efforts including UI/UX, code maintenance, and deployment pipelines (33%) • Evaluate and refine AI models using annotated data and real-world image datasets (10
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in AI-modeling, Monte Carlo simulation, and coding. The successful candidate will work alongside a multidisciplinary team, leveraging artificial intelligence and computational methodologies to optimize
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The SSHRC-funded HubMeta Lab project is undertaking the largest-ever meta-analysis of SME growth studies (over 2,500 full-text articles coded to date). We have uncovered extraordinary variability in
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; implementing and testing ROS nodes, embedded controllers, and closed-loop control routines on prototype hardware; preparing technical reports, delivering presentations to stakeholders, and contributing
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems