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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
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attacks in federated learning. Experience should be demonstrated by participation in projects in this area and scientific publications. Already enrolled in a PhD programme. Minimum requirements: Knowledge
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(PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. Place of work is the Department of Mathematics, Blindern
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and partners (that range from Microsoft Research to the NHS). For this project you should have a strong interest in AI/Machine Learning as well as an ability to develop, build and test interactive
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, applied social science, data science, or related field. 4+ years of progressive experience in computational social science, including data capture, cleaning, analysis, machine learning, NLP, and database
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continuous programme improvement. Participate in educational initiatives and activities to enhance student learning outcomes. Requirements: A PhD or a Master’s degree (with significant industry experience) in
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of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness, brings rich diversity to UBC as a workplace, and
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Engineering Research Associate: Advanced Renewables At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning
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The project will prioritise digitising these records using natural language processing (NLP) and machine learning (ML) to create structured datasets. These will support AI applications in paediatric care
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. • Demonstrated industrial-related experience. • For those that hold a PhD, a minimum of 3 years of relevant industrial experience is preferred. Required Knowledge, Skills, and/or Abilities • Ability to teach all