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requirements. Required Qualifications Ph.D. in Electrical and Computer Engineering, Computer Science Engineering, Biomedical Engineering, Integrated Systems Engineering, Mathematics, Statistics, or equivalent
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volunteers Performs other duties as needed Required Qualifications PhD degree in biomedical engineering, electrical engineering, computer science, biostatistics, or a related field Preferred Qualifications
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 1 hour ago
of the appointment through the ORISE appointment letter and Terms of Appointment. Additional Information For more information on the DOE Scholars Program, visit https://orise.orau.gov/doescholars or contact us at
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Simmons Comprehensive Cancer Center at UT Southwestern Medical Center to study cancer immunology and immune engineering. Our laboratory combines cutting-edge genomic approaches with functional immunology
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computational approaches for high-dimensional data analysis. https://www.epelmanlab.com/ http://www.uhnresearch.ca/researcher/slava-epelman @EpelmanLab This role has direct mentorship and guidance in grant
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experiments, and validation of computational models. Required Qualifications: A successful applicant must have a PhD in Civil Engineering, Engineering Mechanics, or Mechanical Engineering. Applicants
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decision-making in healthcare. The Department of Computer Science and Engineering , a joint department of Chalmers and the University of Gothenburg, spans the breadth of computing disciplines. Our
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ABIF can be found at: http://www.mcgill.ca/abif. Primary Responsibilities 1. Technical Development & Innovation Lead and document quality control (QC) workflows for ABIF imaging systems, including
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team of medical physicists, engineers, and clinicians, and will have unique opportunities to develop and translate advanced computational tools that directly impact patient care in a clinical environment
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multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities