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Field
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project aims to address the current limitations of traditional frame-based sensors and associated processing pipelines with a new family of algorithmic architectures that mimic more closely the behaviours
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Responsibilities: Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop algorithms and prototypes
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algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
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magic, quantum circuits, quantum algorithms (3) Quantum many body computations, including sign problem theory, Quantum Monte Carlo method, DMRG, and machine learning Appointments of postdoctoral
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both the algorithms for robot co-design, and the real-world evaluation of the designs that emerge, as well as providing your own research contributions. Your specific role will vary depending on project
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an extensive array of resources: UAVs for communication protocol, edge computing and control algorithm development; THz transceivers for high-frequency communication channel measurement and sensing; Real-Time
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algorithmic algebra. For more information about the TUM Department of Mathematics, please visit our website: https://www.math.cit.tum.de/en/math/home/. The position is a full-time position (100%), initially
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would be doing: You will be assisting PhD students and a post-doc in developing both the algorithms for robot co-design, and the real-world evaluation of the designs that emerge, as well as providing your
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) optimise BIM software performance and address challenges, including model processing, data storage, and rendering efficiency; and (e) collaborate with AI algorithm teams to understand the business
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Information Engineering and Media to develop Machine Learning and AI algorithms for real-world and media-related applications. The Research Associate/Research Fellow is also expected to support teaching