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Field
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representation learning, and real-world data modeling to stratify risk and optimize MHT formulations. The candidate must thrive in a multidisciplinary, fast-paced research environment and work independently and
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candidate will develop innovative methodologies for data-driven analysis and process optimization, incorporating advanced modeling techniques and real-time analytics to enhance quality control and improve
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tSNE/UMAP clustering and trajectory inference to uncover novel immune cell states and mechanisms. Develops and optimizes new experimental protocols. Summarizes findings and contribute to manuscript
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extended to 2 years. The primary focus of this research will be focused on optimization of multimodal treatment of pancreatic cancer (clinical outcome research). In this role, you will be expected to work
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
and professional sustainability. We optimize our partnership with the UNC Health System through close collaboration and commitment to service. OUR VISION Our vision is to be the nation’s leading public
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digestion), and should have documented capabilities using a variety of simulation and optimization techniques. Familiarity with electric power grids and rate structures is highly valued, as is experience with
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-performance computing. Additional experience in DNA biophysics, machine learning, and optimization is preferred. This job description intends to provide a representative and level of the types of duties and
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algorithms for the scanner using machine learning and deep learning. Qualifications: The position requires some background in machine learning, optimization, and deep learning. Some familiarity with MR physics
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of species, including horses, cattle, pigs, rats, mice, sheep, and birds and to extend efforts at optimizing performance and health in humans and livestock. • Assist with writing grant proposals, manuscripts
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experiment, as well as in the commissioning, optimization, and development of analyses for the ALPHA experiment. The candidate is also expected to take a leadership role in JHU-based testing toward