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(e.g. first-order optimality, gradient descent algorithms, and basic linear or nonlinear programming) is required. Object-oriented programming experience is required. Experience in parallelizing code is
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on electric machine drives and power converters. Our work spans from fundamental modelling and analysis to advanced control design and system optimization. Our specialty is developing embedded control
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• Practical and theoretical experience with advanced control design, particularly for nonlinear or hybrid fluid power and mechatronic systems • Experience with laboratory work and testing of mechatronic systems
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Start Date: October 1 2025 Introduction: This PhD project in Aero-Thermo-Structural Simulation and Optimization of Mechanical Interfaces in Hypersonic Vehicles will be carried out under the UK
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University of British Columbia | Northern British Columbia Fort Nelson, British Columbia | Canada | about 2 months ago
to inform policy decisions that balance economic performance with environmental conservation. Project overview The primary objective is to develop realistic multi-species fish models that determine optimal
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of the research results in major journals and present findings in major conferences. Essential Functions Conduct advanced research on topics related to adaptive and autonomous nonlinear control
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vibration analysis techniques Strong understanding of drilling automation systems and modern control theories, including multivariable control, learning-based control, nonlinear dynamics, convex optimization
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. The project will optimize fishing practices to reduce ecological impacts, providing actionable insights to inform policy decisions that balance economic performance with environmental conservation. Project
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accepted all year round Details Model predictive control (MPC) is a popular advanced control technique that solves a constrained optimal control problem, on-line, at each sampling instant. The first control
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to enhance our multidisciplinary research at the intersection of control theory and machine intelligence. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control, Reinforcement