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work, among other things on song learning in songbirds, hearing in frogs and bats and the effects of anthropogenic noise in marine mammals. Our research uses a number of methods within physiology
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competencies The applicant must hold a master’s degree in engineering and a PhD in a relevant field, such as electrical engineering, with expertise in physics-based modeling, machine learning, and optimization
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. Experience with machine learning techniques for neural data analysis. Track record of publications in high-impact journals and successful grant applications. Work Environment: This position is part of a
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predictive framework linking genomic data to extinction risk, working at the interface of evolutionary genomics, simulation modelling, and machine learning. By integrating forward-in-time simulations, real
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), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres
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publication record relative to their career stage and a clear interest in interdisciplinary collaboration. Ideally, you also bring experience with machine-learning or hybrid modelling approaches, as
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thesis project. Your profile We are looking for a highly motivated candidate with a background in machine/deep learning, and communication networks. The required qualifications include: PhD in computer
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with expertise in digital signal processing methods, and machine learning methods for amplitude and phase noise characterization of optical frequency combs, recovery of dual-comb measurement signals and
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algorithms for speech enhancement using state-of-the-art machine learning techniques. You will design and evaluate models that leverage phoneme-level or discrete speech representations and conduct experiments
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researchers to gather necessary information and carry out assigned tasks efficiently. DIEM - Digital Entertainment Machine is a research project that explores how ordinary people use digital entertainment in