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at conferences. JOB SUMMARY The St. Thomas Center for Microgrid Research (CMR) is seeking candidates for postdoctoral scholar positions in the electric power systems controls, nonlinear dynamics, microgrids, and
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Deadline: (posted 2025/07/10, listed until 2026/06/20) Position Description: Position Description The Tang Group in the UC Berkeley EECS is inviting applications for postdoctoral research and senior visiting
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, including GKP state generation and nonlinear gates. EPIQUE (Horizon Europe): Cluster state generation on photonic integrated chips and its integration into a measurement-based Gaussian Boson Sampling machine
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Control engineering (experience with nonlinear systems is a plus) Machine learning and deep learning in context of physical systems Programming skills are required, with Python experience preferred. A good
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University of British Columbia | Northern British Columbia Fort Nelson, British Columbia | Canada | 2 months ago
, Canada [map ] Subject Areas: Mathematics, Fisheries Management, Cell Migration Appl Deadline: 2025/05/30 11:59PM * (posted 2025/04/10, listed until 2025/05/30) Position Description: Apply Position
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, Electric vehicles chargers, or nonlinear loads Experience in hardware-in-the-loop testbeds and digital twin creation Experience with SEL RTAC 3555 or similar Experience in advanced microgrid controls such as
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. Preferred Qualifications Experience with: C/C++, Python, MATLAB, ROS 1 and 2, OpenCV, Unity, GPU programming, linear and nonlinear control theory, supervised, unsupervised and reinforcement learning, Torch
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optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques to quell opto-mechanical
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developing adaptive numerical schemes powered by advanced nonlinear approximations—like Gaussian mixtures and neural networks. The key challenge? Designing robust and stable numerical schemes that remain
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numerical solution a serious computational challenge. This project aims to tackle that head-on by developing adaptive numerical schemes powered by advanced nonlinear approximations—like Gaussian mixtures and