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candidate will be expected to work onsite as of their effective start date. This position is for a post-PhD trainee preparing for a research scientist career path. The planned position will provide a
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Familiarity with spatial analysis, GIS, or geospatial data workflows. Experience with machine learning, modelling, or systems analysis approaches Interest in resilience, sustainability, and environmental
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 22 days ago
professionals and biomedical researchers from all backgrounds by facilitating learning within innovative and integrated curricula and team-oriented interprofessional education to ensure a highly skilled workforce
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appreciated, including dynamic, stochastic and other quantitative approaches in conditions of uncertainty. Experience in machine learning and other AI methods is a benefit, as is familiarity with the bioeconomy
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security markets, delivering advanced technologies and solutions to both military and civilian customers. Leonardo’s UK Electronics Division specialises in semiconductor growth, device fabrication, and
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through applied research programmes. Faculty in the ICT Cluster undertake funded industry-relevant research, teach courses in Computer Science, Computer Engineering, Information Security and Software
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, and financing, to expand the course offerings to students interested in pursuing this field, or to teach existing courses. This will be a full-time 9 or 12-month appointment at the rank of Associate
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of machine learning techniques, programming and coding for data analysis, biomarker discovery, and collaboration with clinicians and students. Additionally, the candidate will be responsible for grant writing
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Science, or related field Knowledge and experience in computer vision, machine learning, and deep learning Good written and oral communication skills Experience in leading research projects Proficiency in basics
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, machine learning, explainable artificial intelligence (XAI), digital twins, and integrated data-model approaches. • Study of the frugality of the developed approaches by reducing the requirements