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HIL environment rather than only on offline simulation. Learning outcomes anticipated include stronger understanding through immediate feedback on live systems, deeper engagement with
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experimental studies with healthy volunteers using psychophysical methods, psychophysiological measurements, mathematical modelling, and machine learning approaches. RESPONSIBILITIES: Participate in
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electrical and Computers Engineering - Specialization in Automation - lower than 13/20 (1 point); B. Knowledge of Cyber-Physical Systems, Predictive Maintenance Systems, Automation, Machine Learning and
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individuals that make up our community and embraces the opportunity to learn from both our differences and similarities. CPTC values equity and respect. We seek to create an environment of innovation and
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. Provides initial project consultations, refers complex research requests to appropriate divisional subject experts, and develops foundational synchronous and asynchronous learning objects. Partners with
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, Automation, Machine Learning and Artificial Intelligence, Sensor Networks, Hierarchical Decision and Control Systems, with a primary focus on manufacturing and autonomous systems. (1 to 5 points); C
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Information Management, or related fields; Have basic knowledge of machine learning models in supervised and unsupervised learning tasks (i.e., k-nearest neighbours, Decision Trees, Neural Networks, Logistic
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eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please contact the NRC
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points); b) Publications in the area of artificial intelligence, machine learning, computational simulation and multi-agent systems (maximum 5 points); c) Research experience in the project area (maximum
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of ecosystem degradation and the aforementioned extreme climatic phenomena; (v) explore a “Machine Learning” analysis to explore the importance of other environmental and managerial factors in the spatial and