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at the Department of Biosystems Science and Engineering (D-BSSE) of the ETH Zurich in Basel invites exceptional candidates to apply for PhD and postdoctoral positions in pioneering projects developing synthetic
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Mixed Reality. This research combines physiological time series analysis (such as or similar to EEG, EMG, EOG), machine learning, and real-time system design for intelligent interaction systems
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at the Department of Biosystems Science and Engineering (D-BSSE) of ETH Zurich in Basel invites exceptional candidates to apply for PhD and Postdoc positions in pioneering projects at the intersection of synthetic
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informatics, especially chronic disease management (with emphasis on asthma) Machine learning / deep learning / artificial intelligence Profile Ideally, the candidate has a degree in Electrical Engineering and
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with the lab’s objectives. Support the next generation of scientists (PhD students, postdocs), fostering a collaborative and productive research environment. Key areas of support include design analysis
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of this project will bring forward the integration of novel methods at the intersection of advanced control, optimization, manufacturing science, robotics, and machine learning. The doctoral student position we
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that integrate machine learning with energy system optimization to make such policy optimization possible. The analysis will include policies for EV adoption, EV charging infrastructure, and electricity pricing
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master’s degree preferably in Computer Science, Information Technology, Information Systems, Statistics, Data Science, Engineering, or a related field Strong machine learning and programming experience with
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the admission requirements for a PhD at ETH Zurich Experience in machine learning, optimization, or AI-driven decision-making Preferably with knowledge of Bayesian optimization or Gaussian processes
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We are seeking a highly motivated and interdisciplinary Postdoctoral Researcher to join our team in developing a cutting-edge nutrient prediction platform. This project integrates machine learning