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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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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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decision frameworks to advance knowledge on how complex production systems are developed and reconfigured over time. Job description The position contributes to the group’s strategic development toward
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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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the evaluation of converter and drive architectures for edge-resident intelligence; the development of advanced modulation, estimation and control algorithms for high-performance operation; and the integration
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will be to develop novel or improve existing AI algorithms and conduct investigations aimed at improving our understanding of the evolution and function of microbiomes. You will be responsible
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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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advanced modelling, data analysis, and algorithmic development. Your tasks will support our core research focus of mathematical and computational approaches to design and implement solution algorithms, with