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Field
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algorithms to develop cybersecurity, optimization, and control solutions for real-world grid applications. Candidates will be required to work in at least 4 of the following areas: Build, simulate, and
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computational models and systems using algorithms and analytics for materials and related physical sciences for a broad range of energy, transportation, and advanced manufacturing applications. Major Duties
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the complexities of the human regulome through advanced cell-free DNA profiling and developing cutting-edge computational algorithms and molecular profiling techniques. Our research focuses on early cancer detection
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driven individual with a PhD in data science, computer science, biomedical informatics, or a similar background with some experience working with large datasets. Prior experience with healthcare is not
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University. This research opportunity will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including single-cell RNA
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, shaping the future of medicine through cutting-edge research. We seek a motivated and talented Postdoctoral Fellow to join Dr. Andrei Rodin’s laboratory in the Department of Computational and Quantitative
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optimization of optical imaging hardware, develop data acquisition software and algorithms for data processing, as well as perform phantom and human clinical studies. This candidate is expected to co-supervise
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not required as part of this position. Required Qualifications: Strong mathematical background, including expertise in one or more of the following areas: machine learning, statistics, and algorithms
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position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate
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Computer Science 2. Demonstrated, successful experience (academic or professional) programming in algorithm level languages such as Matlab/Simulink, Python or similar 3. Demonstrated, successful experience (academic