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learning, offers powerful solutions to automate these tasks and provide reliable real-time information. This doctoral project is part of a 5-year research chair on Computer vision applied to the swine sector
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engineering, electrical engineering, data science, or a related field. Skills in embedded systems development, electronics, or IoT (C/C++, Python, Arduino/ESP32, etc.) OR in machine learning and sensor data
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The University of British Columbia (UBC) | Vancouver UBC, British Columbia | Canada | about 10 hours ago
experience in optimization, machine learning, control systems, or robotics is desirable. No other specific qualifications beyond and a willingness to learn within an interdisciplinary team. If you have any
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series analysis, machine learning approaches). Ability to apply data analysis and simulation skills to generate insights into operational changes or targeted retrofits that will enhance building energy
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spatial omics datasets. The position will also contribute to multi-modal data integration efforts that combine imaging, genomics, and machine learning approaches. Key Responsibilities Data Processing
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control subjects based on diffusion MRI images and functional MRI responses. Duties include: Developing machine-learning and/or deep learning pipelines for classifying patients of optic neuropathies and
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rapid and significant shifts in power consumption, next-generation high-performance computing (HPC) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs
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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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of transmission electron microscopes and/or scanning electron microscopes Knowledge of artificial intelligence and machine learning and their applications in electron microscopy Knowledge of computer programming
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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