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Field
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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welcomes applications from individuals with a master’s degree in the following fields: Computer Science Mathematics Natural or engineering sciences or an equivalent degree, interested in High-Performance
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represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster networking, enable internationalization and
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DEPARTMENT The Department of Electrical and Computer Engineering at UTEP (http://ece.utep.edu) offers Bachelor of Science (B.S.) and Master of Science (M.S.) degrees in Electrical Engineering and in Computer
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: SIMD performance engineering. Machine Learning. Communication-efficient
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable
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seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking
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. The project will be part of the activities of the Constellium University Technology Centre (UTC) established with BCAST. The successful candidate will have the opportunities to interact with researchers in
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description This project addresses the effective design of a military supply logistics network, composed of transportation and communication links such as roads and rail, aerial drone routes, and nodes, such as
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading