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Field
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to develop novel machine learning methods to improve malware detection. The doctoral student position is offered within a research project financed by the Wallenberg AI, Autonomous Systems and Software Program
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
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engineering (focusing on deep learning for computer vision), and the division of statistics and machine learning at the department of computer and information science (focusing on the theory behind machine
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science, data science, applied mathematics, physics, materials science, or a related field. Solid background in machine learning and/or computer vision. Interest in representation learning, active learning
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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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A Human-Factors Investigation of Automation, Decision-Support and Machine Learning in Clinical Decision-Making Tasks. This PhD project is based within the Human Factors Research Group in the Faculty
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using multimodal approaches including advanced imaging, nano-mechanical characterisation and machine learning techniques Developing physics-informed reliability models using experimental datasets
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This exciting opportunity is based within the Power Electronics and Machines Control Research Institute at Faculty of Engineering which conducts cutting edge research into power electronics
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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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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