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will explore how semantic digital twins can automate the generation of control-oriented models and how LLMs can facilitate natural language interaction with complex building management. The developed
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of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD project falls under the collaboration between
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, computer science, and statistics The objective of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD
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science background, preferably in algorithm design, system programming, networking, operating system, and computer architecture. Experience in low-latency RPC, network stacks, distributed systems, host data plane
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to design adaptive, efficient, and intelligent algorithms for hearing assistive devices. Key objectives include improving speech perception in noisy and unpredictable environments, reducing listening effort
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models to design adaptive, efficient, and intelligent algorithms for hearing assistive devices. Key objectives include improving speech perception in noisy and unpredictable environments, reducing
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-and-egg" problem of sampling: developing algorithms that learn to bias simulations automatically without requiring prior knowledge of the mechanism. You will contribute to open-source software
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Job Description Are you interested in the interface of technology and the human body? Are you eager to contribute to a better life for patients suffering of profound hearing loss by developing novel
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. The PhD students will work on several tasks, including: development of safe data-driven control/reinforcement learning algorithms to recover parameter identifiability by exploration of different
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, enthusiasm for technology and the drive to adopt new skills and extended responsibilities. We offer an open, international team with flexible work organization and support of individual development. The group