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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Proposed Start Date 03/16/2026 Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching, research and public service
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are in compliance with the necessary trainings (both at the lab and at the institutional level). Minimum Education and Experience: A PhD degree in Computer Science, Electrical/Computer Engineering, or a
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conditions. 2. Must be able to work at a computer for extended periods for data analysis, and manuscript preparation. 3. May require routine work with biological samples, including recombinant proteins and
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artificial intelligence with a preferred focus on computer vision and medical image analysis. Preferred Qualifications: PhD in Medical Physics, Bioengineering, Biomedical Engineering, Physics, Computer Science
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of 3D+ structure, heterogeneity, and/or dynamics from scattering and/or microscopy data; (2) autonomous analysis and decision making for self-driving and/or human-in-the-loop experiments; (3) computer
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. Teamwork: Ability to work collaboratively with others and contribute to a team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications
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statistical methods, prepare manuscripts for publication in scientific journals and presentations for laboratory meetings and national conferences, assist with grant proposals, protocols, and compliance reports
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://pritykinlab.princeton.edu) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi-modal single-cell, spatial and genome editing
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the development of novel reconstruction methods that can concurrently estimate the sound speed and initial pressure distributions within tissue from PACT data. Reconstruction methods for ultrasound computed
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Methods and Dynamics (MMD) Group at Oak Ridge National Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to Computational Methods for Data Reduction. Topics