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computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate hydraulic flood
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research and experimental data in accordance with established protocols. Assist in statistical and comparative analysis of experimental data using appropriate computer software. Contribute to the preparation
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Two Postdoctoral Researchers in Cell Delivery-Based Beta Cell Replacement Therapy for Type 1 Diabete
applications, often taking on an interdisciplinary character. Cutting-edge contributions to areas such as computer systems, theoretical computer science, cybersecurity, computer vision, artificial intelligence
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expression of information (genetics), its translation into the workings of individual cells (cell biology), and the assembly of cells into tissues and organ systems (development). Our vision for the future
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Employee and Children Employee and Family coverage Dental & Vision Life Insurance, Accidental D&D, Long Term Disability, Flexible Spending Account and Day Care Spending Account DUAL CAREER PARTNER PROGRAM ON
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of statistical physics, information theory, and computational modeling. RESPONSIBILITIES The Postdoctoral Research Associate will perform basic or applied research of a limited scope, primarily using existing
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
one of the leading public research institutions in the country. UNC-Chapel Hill offers postdocs comprehensive medical and vision coverage , paid leave, and benefits and services that support
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), research is carried out in computer vision, robotics and machine learning. We are now looking for two postdocs in robotics and machine learning and computer vision. The successful candidates is expected
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Deadline 30 Nov 2025 - 12:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and