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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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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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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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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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, 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
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, the successful candidate will: Create an inventory of hanging glaciers using manual mapping and possibly machine-learning aided approaches Perform field measurements at high-elevation sites in
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transport characterization Using first-principles calculations and machine-learning foundation models during the initial stage of the project to identify promising metastable compositions for experimental
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Gallery and the EuroTube Foundation. Profile A Master’s degree in civil engineering, geomatics, computer science, or a related field A strong background in machine learning, computer vision, or 3D