25 condition-monitoring-machine-learning Postdoctoral positions at The University of Arizona
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Experience with super-resolution ultrasound, US localization microscopy, photoacoustic imaging, elasticity imaging, pulse encoding, solving inverse problems, machine learning, AI, SolidWorks, 3D printing FLSA
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any of the following areas is desirable Collection and analysis of dense nodal seismic datasets Numerical simulations of the seismic wavefield High-performance computing Machine learning applications in
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equitable scholarly environment in research, mentoring, and service. Your work will focus on the SEAMLESS (SEmi-Automated Machine LEarning Search for Semi-resolved galaxies) survey, whose goal is to identify
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clear, concise manner orally and in writing. Ability to create clear presentations and use strong interpersonal skills with a collaborative mindset. Ability to effectively teach, mentor, and guide
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Location Greater Phoenix Area Address 475 N. 5th Street, Phoenix, AZ 85004 USA Position Highlights A postdoctoral position is available to teach gross anatomy in the Department of Basic Medical Sciences
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internal state and environmental structure shape their functional operation. To address these questions, we use an observational and causal approach, combining monitoring and decoding of neural activity with
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characteristics, including demographics, measures of cognitive function, and clinical health conditions and risk factors. Experience in Matlab or other programming routines and languages. Experience with
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of a team focused on characterizing CAIs down to the atomic level using electron microscopy and comparing the characterization data with model output to infer the thermodynamic conditions and chemical
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sequence programming (e.g., IDEA/ICE) and contemporary image reconstruction techniques (e.g., compressed sensing, parallel imaging, model-based or deep learning reconstructions). Knowledge of radial data
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sequence programming (e.g., IDEA/ICE) and contemporary image reconstruction techniques (e.g., compressed sensing, parallel imaging, model-based or deep learning reconstructions). Knowledge of radial data