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Field
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Challenge grant. The successful candidate will work closely with the Principal Investigators (PIs) to develop and implement innovative research integrating machine learning, computer vision, and wildlife
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fine printing experience Candidate must have exceptional craftmanship and artistic vision Candidate must be a permanent U.S. resident, current UNR affiliates are not eligible Preferred Qualifications All
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biology, machine learning, and predictive modelling; translational studies integrating laboratory discoveries with clinical implementation. Vancouver is a vibrant coastal city located on the traditional
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may include, but are not limited to: Artificial Intelligence and Machine Learning in Art History Museums, Collections and AI 3D Scanning and Digital Heritage Extended Reality (VR, AR and Mixed Reality
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should possess: PhD/Ms/BSc in Computer Science, Artificial Intelligence, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep
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machine-learning, predictive-modelling or multivariate methods to behavioural and/or EEG data. Proficiency in R, MATLAB and Python. What we can offer In return we offer a generous pension, relocation
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for: Conducting research and development on AI-based solutions for automated defect inspection and condition assessment of train components by designing and developing deep learning, computer vision, and machine
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to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural problems that also depend
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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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applications for a PhD Research Fellow position in fairness in artificial intelligence, available at the Department of Informatics, in the Scientific Computing and Machine Learning (SCML) research group