72 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Canada
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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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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | 17 days ago
sustainable development. Collaborations with Mathematics and Computer Science The post-doc will also affiliate with Lund's Centre for Mathematical Sciences, renowned for research in machine learning
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Council of Canada). The research will focus on applying, developing, and implementing novel statistical methods for causal inference, integrative data analysis, and machine learning with large
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Overview On this page Objective The Canada Postdoctoral Research Award (CPRA) program recognizes and supports the next generation of outstanding innovators, knowledge workers, creative thinkers and
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allows for translation of novel methods to our cancer clinics in Canada (Vancouver, Victoria, and Kelowna) and beyond. Requirements: The ideal candidate will have a PhD in computational modeling/oncology
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this will include: Demonstrated expertise in data analysis and simulation Familiarity with C++; and proficiency in the use of ROOT and Geant4, and interest in machine learning techniques Knowledge
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? Postdoctoral Research Opportunities in Science (PROS) at Baylor College of Medicine allows you to meet faculty and trainees from multiple labs and learn about our resources in the Texas Medical Center in
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worldwide, leveraging industry-standard tools and technologies to ensure the quality and reliability of the developed prototype hardware implementation. Qualifications: PhD in Electronics/Computer Engineering
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in motor circuitry to motor performance and learning (see PDF). This CIHR-funded research program is comparative, involving experiments in songbirds (zebra finches) and mice, and conducted in
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electrophysiological techniques AND/OR neuromodulation techniques Background knowledge in electronics and experience in signal processing. Background knowledge of machine-learning, AI, and computational neuroscience