104 electrical-machine-"https:"-"https:"-"https:"-"https:"-"https:" uni jobs in Netherlands
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- European Space Agency
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Are you interested in designing innovative separation methods that use an electric field as an additional driving force? Can you combine experimental with simulation results into a design that is
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) . You are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity/research for the traineeship About 21% of the world’s forest area is currently designated under legal protection
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students, and contributing to an open, diverse, and inclusive academic community. Job requirements You hold a PhD in Computer Vision, Machine Learning, Artificial Intelligence, Computer Science, Electrical
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, you will: combine knowledge on electrostatic interactions, fluid dynamics and machine design; design a novel, scalable, separation process for proteins that uses electric fields as a driving force
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, enabling trustworthy computation without compromising energy budgets. Treating security as a first-class citizen when designing hardware components and future computer architectures requires novel design
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of information on all the minerals found on the Moon, Mars and in meteorites, and the Machine Learning (ML) software that combines deep learning multi-class and multi-label classification algorithms
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of high-quality GPR data collected at the University of Twente’s Utility Mapping Site (UMS), a unique test environment for utility mapping technologies. Current machine learning models and their training
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environment for utility mapping technologies. Current machine learning models and their training data are limited in size, comprehensiveness, and realism – resulting in partial automation with limited
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Are you inspired to make textile production digital, circular, and scalable for SMEs? Are you eager to turn machine, production, and ecosystem data into insights with data science, ML, and AI
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explore emerging areas including integrated sensing and communication (ISAC), large-scale antenna systems, reconfigurable intelligent surfaces (RIS), near-field communications, machine learning