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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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that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps
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is in our approaches to peer-to-peer learning and student leadership by empowering students to work collaboratively with service areas and other students to grow holistically as individuals.The
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 10 hours ago
development and accreditation. Responsibilities: The successful candidate will teach six undergraduate courses per year in the Computer Engineering curriculum. Courses may include: Computer Architecture
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plan and implement components of programs and activities and will be responsible to conduct detailed analysis on program efficiency and student learning outcomes, which will inform program planning
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network connectivity. Implement machine learning models to detect patterns in large datasets and assist in predictive modeling. Develop and maintain custom scripts in Python or MATLAB to automate data
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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 and students. Our commitment
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training, and experience in an academic teaching center will be considered. Candidates with additional specialized training in machine learning, robotics, or pre-hospital medicine are preferred
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 10 hours ago
metabolomics] research, with experience in the use of techniques in machine learning, statistics, algorithm engineering, bioinformatics, or advanced data visualization. Evidence of effective participation and
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) and dynamic spread models for invasive forest and agricultural pests, integrating multi-source ecological data using machine learning and statistical approaches. Develop and validate phenological models