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Learning Course Description: Machine Learning applications are increasingly utilized to make crucial decisions in many sectors of our economy and society. These include, but are not limited to, healthcare
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 8 hours ago
-level service activities. Promote an inclusive and supportive learning environment for undergraduate students. Qualifications: A PhD in Electrical and Computer Engineering or a closely related discipline
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initiatives, or with programming language experience and experience in machine learning and health informatics. An understanding of the digital health space, is expected but not essential. Must have previously
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(inclusive of PhD and/or post-graduate work) in accelerated research and development in the area of development and application of machine learning/deep learning methods for biological or chemical data
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) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs
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industry/upskilling educational programs, course designs, and developing workshops for STEM subjects, including but not limited to machine learning, robotics, laboratory automation, and materials discovery
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(HPC) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution
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training and guidance to junior undergraduate and graduate students. Education: A PhD in Neuroscience, Computational Neuroscience, Machine Learning in image analysis, or a related field, with significant
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epigenetics, genomics, multiomics, big data and machine learning an asset. · Is willing to learn new techniques and novel analysis methods for application. · Skills in R required. · Experience
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vacancy at the University. At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff