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optimization, including integer, nonlinear, and combinatorial optimization; global and non-convex optimization; machine learning for optimization; explainable artificial intelligence; heuristic and metaheuristic
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with provable performance for nonlinear systems. About us The Department of Mathematical Science provides a creative, dynamic and innovative environment where research, education, and societal
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of the PhD project is to develop an effective control structure and real-time optimization scheme for a novel continuous bioprocess. The control and real-time optimization will then be validated in experiments
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) for exploration and inspection Soft and morphing aerial robots with pneumatic actuation Navigation in GPS-denied, cluttered, or dynamic environments Advanced Control and System Integration Nonlinear, adaptive, and
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, and nonlinear optics. We are developing efficient X-ray optical elements to build up the functionalities by controlling the 3D structure of materials over large volumes and on nanometre scales, such as
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Innovative electrical interfaces for solid-state electrocaloric cooling using ferroelectric ceramics
exchange between the active material and the environment, 2) the energy-efficient control on the electrical side and 3) the development of a full proof of concept in order to demonstrate the device
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Engineering at NTNU, where computational mechanics, advanced finite element modelling, and artificial intelligence meet. As a PhD candidate, you will work at the forefront of nonlinear simulation, contributing
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of interest include: Robot modelling, Nonlinear and Optimal control, Reinforcement learning, and Data-driven modeling and control. The Post-Doctoral associate will be based at NYU Abu Dhabi and will directly
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analysis, machine learning, embedded DSP, digital design, logic synthesis, computer architecture, embedded systems, robotics, nonlinear and hybrid control systems, intelligent transportation systems
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-network computing. The work includes modeling coupled nonlinear dynamical systems, developing learning and inference schemes for neuromorphic/reservoir and Ising-type computation, and benchmarking