94 assistant-professor-computer-science-data-"https:"-"https:"-"https:" positions at EPFL
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Institutes and Universities: https://www.nccr-separations.ch We invite applications for a Tenure Track Assistant Professor in chemistry and chemical engineering with a strong link to computational modelling
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be developing new methods for remote sensing. Profile PhD in Computer Vision Background in Computer Science Research experience in 3D Computer Vision, remote sensing and aerial imagery Strong
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Bioengineering Technologies. Appointments will be at the Tenure Track Assistant Professor level. We seek exceptional individuals who will develop and lead a research program at the forefront of the
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for a faculty position in Women’s Health Engineering and Digital Technologies. Appointments will be at the Tenure Track Assistant Professor level. We seek exceptional individuals who will develop and
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specifications, proofs, and verified artefacts Engage with the ARIA programme, including red/blue team exercises and sprint reviews Profile PhD (or nearing completion of) in computer science or a closely related
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learned control policies and neural representations of movement in collaboration with experimental labs Profile Common requirements: PhD (completed or close to completion) in computer science, applied
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physics, engineering, computer science, or a related field. Strong scientific programming in Python and experience with GPU processing of large-scale datasets. Experience with inverse problems and 3D
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. Candidate profile Applicants should have a PhD in biomedical engineering, electrical engineering, computer science, data science, computational neuroscience, human movement science, psychophysiology
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particles, air pollution, exposure science, environmental measurements, or pollutant modelling; A strong aptitude for experimental and/or computational research; Experience with quantitative data
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background, and an outstanding MSc degree in Engineering, Computer Science, Physics, Applied Mathematics, or a related field. You should be proficient in or willing to learn generative deep learning – in