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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 9 hours ago
interpretable artificial intelligence to improve prediction accuracy while ensuring biological interpretability. The candidate will contribute to building computational frameworks that integrate diverse imaging
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buried in the sediment. The aspects to be investigated here are the interaction of frequency-dependent propagation and imaging effects in conjunction with the signal processing with regard to image
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: Knowledge or experience in the development of metrology systems using lasers and image processing. Contracting requirements: Presentation of the academic qualifications and/or diplomas, if applicable
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Post-Doctoral Associates; anticipated start date is November 1, 2025. Description of positions is as follows: Performs professional entry-level structural and functional magnetic resonance imaging (MRI
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, crop production and plant breeding. A core responsibility will be the rigorous processing, analysis, and interpretation of large and complex datasets and, in collaboration with the FEM research community
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mandatory requirements: - Bachelor's degree in Geospatial Engineering, Information Technology, or related scientific fields; - Experience in optical image processing and using Geographic Information Systems
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of precision imaging datasets using supercomputing cluster resources. Support students and trainees involved in data processing and analysis. Present novel discoveries at international conferences. Publish
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of neural activity will be used in conjunction with advanced imaging methods and behavioural assays to investigate how signals beginning in the retina and transmitted through different visual pathways
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superconductivity with hybridization at a superconducting-semiconducting interface. [4] Sivakumar et al., Phys. Rev. B 111, 075409 (2025), Influence of surface relaxations on scanning probe microscopy images
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deep learning techniques to improve image processing and trait prediction. Analyze large datasets generated by the Phenomobile.v2+ to identify key traits affecting crop performance under stress