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Field
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of developing algorithms that are both technically robust and clinically relevant, ensuring that these innovations can be integrated seamlessly into existing imaging systems and workflows. Collaborating with
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applications across a wide range of imaging and video processing fields beyond medical imaging. The Research Associate will be at the forefront of developing algorithms that are both technically robust and
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and conferences. Proven experience in design and implementation of deep learning algorithms. Outstanding programming skills in Python. Extensive experience working on one or more of the following areas
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the temperature-dependent polarization of pyroelectric materials to measure absorbed dose in real time. The Fellow will optimize sensor materials and designs, refine calibration methods, and validate performance
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, quality assured manner. work independently with a minimum of supervision; supervise staff and students in the field; maintain meteorological, snow and hydrological sensors and mechanical equipment including
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candidates with computational tools and machine learning algorithms, and elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc. Tasks include
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media, newsletters, and event promotion. Ability to research and adapt to evolving social media trends, algorithms, and digital marketing techniques. Experience creating and managing content calendars and
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multimodal data (video, self-report, physiological sensors, automatic facial recognition software) that examines CER in multiple contrasting contexts. And (3) Advance educational theory and practice by
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred