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Field
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of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD project falls under the collaboration between
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foundation in either machine learning or mathematical/computational neuroscience, demonstrable programming experience (Python/PyTorch), and the curiosity to work across disciplinary boundaries. A background in
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The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for PhD stipends or integrated stipends in the field of Machine Learning for Intelligent Hearing
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, materials science, and physics. Supported by 19 countries, the ESRF is an equal opportunity employer and encourages diversity. Context & Job description Thesis subject: Machine Learning for Neutron
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, computer science, and statistics The objective of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD
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- When and where do we reach the limits of adaptation to riverine flood risk?”. You have experience in machine learning, programming and flood risk research. If so, we encourage you to apply! You will
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to MINA’s PhD programme. The documentation that is necessary to ensure that the admission requirements are met, must be uploaded as an attachment. Main tasks Develop machine learning models to produce forest
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/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 packages
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Department. Profile You hold a master’s degree in Electrical Engineering, Computer Engineering, Telecommunications, Computer Science, or a closely related discipline, or you will have obtained it by the time
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the research profile of the University of Bremen. Rooted in computer science, Minds, Media, Machines connects researchers from eight faculties of the university with numerous internal and external partners