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Position overview Position title: Assistant Program Director and Head of General Collections Metadata Salary range: A reasonable salary range estimate for this position is $66,160-$159,380
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State University of New York University at Albany | Albany, New York | United States | about 3 hours ago
to engage in cross-institutional networking at the systems and community levels to expand YJI's network of research partners and opportunities. They may be asked to attend and or present at conferences and
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research students’ development is supported by a programme of high quality internal training, a dedicated and cohesive team, and exposure to our extensive network of international colleagues. They also have
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combines an MSc and PhD project in a 1+3+1 format. You can create your own project, source a supervisor and they will choose an MSc programme that aligns with your research proposal. Please review the How
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) Physical exercise intervention in depressive disorders: Meta-analysis and systematic review. Scandinavian Journal of Medicine & Science in Sports 24, 259-272. Contact Dr Craig Melville, University of Glasgow
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to understand brain networks at multiple levels of function, from cells to cognition with a strong emphasis on imaging and computational analyses of each level. Our translational efforts are directed at a range
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to Control Systems, Robotics, Electronic Warfare, Electromagnetics and Antennas, Embedded Systems, Computer Systems, Machine Learning, Microelectronics, VLSI, IC Design, Communication and Networks, or any
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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based on electronic medical records. It is also attractive for novel applications, e.g. multimodal applications in meta-verse, which have little data for training and evaluation. This project focuses
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include learning to learn and meta-learning, active learning, semi-supervised learning, multi-task learning, transfer learning, and learning representations for NLP. Techniques include deep generative