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. We offer a dynamic environment where theory meets real-world application, and where your work can contribute to the development of the next generation of smart energy systems. About us The Division of
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the Division of Data Science and Artificial Intelligence and the employment is with Chalmers University of Technology. The division’s research spans from foundational machine learning theory to applications
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questions about the particles and forces governing our Universe to energy-related research. The methods of our investigations are also diverse and complementary, and range from theory and computer simulations
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application! Work assignments Our current research projects focus on distributed radar sensing, hardware-efficient signal processing, robustness and resilience, and communication-efficient decentralized machine
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experiments at synchrotron sources. The project is funded by the Chalmers Area of Advance Nano, and involves close collaboration with the groups of Sophie Weber (theory, physics) and Saroj Dash (experiment
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sources. The project is funded by the Chalmers Area of Advance Nano, and involves close collaboration with the groups of Sophie Weber (theory, physics) and Saroj Dash (experiment, physics), as
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power grids. In this role, you will combine theory and experimentation to address one of the most critical challenges in modern energy systems, maintaining stability in an increasingly converter-dominated
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Optimal Control Theory Strong programming skills in C++/Python/MATLAB Familiarity with parallelization and high performance computing (CPU and GPU friendly code) Experience with Machine Learning, generative
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design, and/or machine learning in the context of integrated photonics. We are looking for someone who wishes to work theoretically in this field, while still maintaining close contact with experiments
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We are looking for a postdoctoral researcher who wants to contribute to the development of next-generation frameworks for resilient power grids. In this role, you will combine theory and