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The University of Nevada, Reno (UNR) appreciates your interest in employment at our growing institution. We want your application process to go smoothly and quickly. Final applications must be
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the School of Architecture and Associated Faculty of the Department of Computer Science. The desired start date is Spring 2025. Appointments are for one year with the possibility of renewal pending
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also be explored during the later stages of the selection process. The working languages of the Agency are English and French. A good knowledge of one of these is required. Knowledge of another Member
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Proficient in platforms/tools/languages such as Python, Matlab, QGIS, Google Earth Engine, version-control, C++ processing large datasets Preferred Qualifications: Experience with transdisciplinary work in
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will have a background in machine learning and natural language processing (NLP), with a demonstrated ability to work with large language models (LLMs) and unstructured clinical text from electronic
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
, computer vision, or natural language processing) though exceptional candidates with other experience will be considered. Track record of publications in premier technical and/or biomedical venues. Previous
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Faculty of the Department of Computer Science. The desired start date is Spring 2025. Appointments are for one year with the possibility of renewal pending satisfactory performance and continued funding
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
Experience Requirements Applicants should have (or expect to receive before the start of the position) a PhD in Computer Science or EE/ECE on a topic in machine learning, artificial intelligence, natural language
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wellbeing. They will use modeling approaches to combine natural and social science data. You will focus on developing and implementing mixed modeling approaches to test pathways through which structure
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI