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research oriented towards applications of quantum computing, quantum algorithms, or machine learning. Publishing articles in top-tier journals and disseminating results on thematic conferences. Applying
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brings complementary and/or additionally new expertise including AI/Machine Learning-based methodologies that can be developed for virtual ligand screening, reverse virtual screening (target fishing) and
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Adaptive Learning in Brain-Robot Interactions School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Mahnaz Arvaneh Application Deadline: Applications accepted all year
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Engineering, or related fields. They must have proven experience in the development and programming of Information Systems; prior experience in applications based on Data Analytics/Machine Learning and data
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/Planning Internal Number: 7006956 Adjunct Faculty - Architecture About the Opportunity The Lecturer will teach introductory courses in architectural drawing, sketching, studio design, computer modeling
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Department of Cell and Molecular Biology (ICM) (https://icm.uu.se ) is
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into the School's activities. We are particularly interested in candidates with expertise in Digital Health and AI in Medicine, including machine learning (especially deep learning), natural language processing, and
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Qualifications: Completed doctoral studies – PhD in bio-resource technology, practical implementation of Machine Learning, or a related field. Strong knowledge of Food security theory. Understanding of principles
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models remain a limiting factor in moving to a quantitative scale. Molecular simulation has benefited from recent advances in machine learning and generative artificial intelligence to such an extent
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and adapt machine learning and deep learning models (e.g., convolutional and transformer-based architectures) to biological questions in collaboration with investigators. Develop interpretable models