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Field
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- your research will result in deployable system prototypes, cutting-edge algorithms, and publications in top-tier venues. You’ll be part of a collaborative environment that values innovation
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collegiality is at the heart of what we do. The Department’s research structure consists of five Research Groups: Algorithms and Complexity (ACiD) Artificial Intelligence and Human Systems (AIHS) Network
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and fine-tuning frameworks would be welcome. Proficiency in software engineering. A good understanding of data structures and algorithms and data-oriented approaches to problem-solving. Excellent
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radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical imaging. Within our research
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developing machine learning algorithms specifically designed for medical imaging applications. In addition, performs analysis of tissue images of cancer using machine learning methods that have been prototyped
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours ago
/or machine learning/artificial intelligence algorithms. Projects may also include work focused on the analysis of spatial and geographic data and work extrapolating results to different spatial scales
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and existing tools to interpret, analyze, and visualize multivariate relationships in data. Create databases and reports, develop algorithms and statistical models, and perform statistical analyses
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experience from which comparable knowledge is demonstrated may be considered. Preferred Qualifications Ph.D. degree. Demonstrated expertise in computational biophysics and molecular dynamics (algorithms and
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disease patients using radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical
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trustworthy medical AI? Deep models already outperform humans on many benchmarks, yet in the clinic they remain black boxes: radiologists cannot see why an algorithm flags a lesion, and AI engineers cannot tell