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lab's existing machine perfusion platforms for human and rat liver This will include learning the engineering design, assembly, and operation of the perfusion hardware; developing improved control
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Learning, Digital Humans, Earth Observation, Educational Technology, Efficient AI, Explainable AI, Haptics, Human-Computer Interaction, Human-Robot Interaction, Imaging Technology, Machine Learning
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monitoring data Artificial intelligence and machine learning for engineering applications Researchers in COMBINE benefit from interdisciplinary, international,and inter-sectoral training, including secondments
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platform , published in Nature Communications (https://www.nature.com/articles/s41467-020-18059-7), that enables localized, hyper-efficient delivery of therapeutic compounds to specific brain regions
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or will complete a Master’s degree in computer science, machine learning, statistics, physics, engineering, applied mathematics, or other relevant fields, at least 3 months before the beginning of
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Mechanical and Process Engineering (D-MAVT) of the ETH Zurich, in the Zentrum Campus in Zurich, Switzerland. D-MAVT (https://mavt.ethz.ch/) is an interdisciplinary department with focus areas in process
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Workplace We offer The position is hosted in the Department of Mechanical and Process Engineering (D-MAVT) of the ETH Zurich, in the Zentrum Campus in Zurich, Switzerland. D-MAVT (https://mavt.ethz.ch/) is
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, Computer Science, Mechatronics, Remote Sensing, Engineering Geology or other related discipline Demonstrated expertise and interest in machine learning and computer vision algorithms is necessary, with
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, optimization, and learned surrogate models to design and fabricate efficient, bioinspired robots. Reinforcement Learning and Machine Learning: data-driven methods that let robots learn new skills and
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modelling Python, C# or equivalent language expertise Basic knowledge of machine learning or deep learning Desirable Skills Exposure to musculoskeletal modelling Familiarity with containerization or