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Field
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of both science and societal application. We contribute to innovative information technologies through the development and application of new concepts, theories, algorithms, and software methods. With our
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control and energy-management algorithms for efficient and reliable long-duration operation. Implement hardware-in-the-loop (HIL) and real-time co-simulation for validation under realistic grid scenarios
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interconnected nature of these systems demand advanced modeling and control tools that leverage their distributed and structured characteristics for effective optimization and performance. Information As an
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in algorithmic design; Possess good communication skills and an excellent command of English. Additional Information Benefits We encourage high responsibility and independence while collaborating with
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of hyperbolic deep learning and one PhD student with a keen interest in the algorithmic side of hyperbolic deep learning. Tasks and responsibilities: Conduct high-impact research on hyperbolic deep learning
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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning
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DSP algorithms for FSO communications.
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adaptability, and safety; Applying AI and optimisation techniques (e.g. reinforcement learning and evolutionary algorithms) to adapt locomotion strategies to varying surface conditions; Supporting
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the entire system, where many interconnected modules affect each other. In this project, you will be designing algorithms to guarantee the reliable operation of semiconductor machines, together with a highly
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tagging algorithm development as well as physics data analysis, with a focus on Higgs boson physics, top quark physics, and searches for new physics signatures. This is what you will do After the discovery