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written and spoken. Experience with experimental fluid mechanics and computer vision is an advantage. Our offer We offer a stimulating, multidisciplinary research environment within the ETH Domain, with
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generation of social, computer-based, and cyber-physical systems that make a substantial contribution to the welfare of our society, for example, via embodied intelligent systems that are tailored to users
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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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: MSc in Physics, Materials Science, Nanoscience, Computer Engineering, Data Science, Gaming Engineering or a related discipline. · Knowledge: Strong coding skills in Python and knowledge in materials
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is offering multiple studentships for candidates from backgrounds spanning the physical and computer sciences. These students will develop core expertise in robotic, digital, chemical and physical
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qualified women. About the position The position contains both teaching duties and participation in research projects. The research project topics focus on improving object recognition through computer vision
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science, engineering, mathematics, or related subject) Proficiency in English (both oral and written) Essential to have strong foundations in computer systems through degree courses or equivalent work experience
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properties through computer simulations. The project also includes validation of materials in the lab and translating the findings into practical recommendations for use in real power-electronics environments
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of Engineering), Mike Pound (Computer Vision, Computer Science Department), and Darren Wells (Plant and Crop Biophysics, School of Biosciences). Who we are looking for An enthusiastic, self-motivated, resourceful
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(particularly cognitive or applied psychology) Cognitive Science Human–Computer Interaction Engineering or Computer Science Health sciences Experience in empirical research, experimental design, data analysis