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Leibniz Institute of Ecological Urban and Regional Development (IOER) • | Dresden, Sachsen | Germany | about 12 hours ago
Dresden University of Technology (TUD) Course location Dresden In cooperation with Dresden University of Technology (TUD) Teaching language English Languages The programme is conducted in English. Full-time
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and computational abilities • Demonstrate excellent programming ability in languages such as MATLAB or Python • Excellent communication skills across multiple disciplines • Excellent academic
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on an important topic in a well-funded multi-disciplinary international training network. The training involves multiple activities, in addition to your research, and secondments across our partners. Overview
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University Hospital, Duke Regional Hospital, Duke Raleigh Hospital, Duke Health Integrated Practice, Duke Primary Care, Duke Home Care and Hospice, Duke Health and Wellness, and multiple affiliations. Job
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development. As a student in the PhD Programme Science and Technology , you will work towards your doctoral thesis and earn your PhD upon successful defense of your thesis. Main tasks Description of tasks
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NMBU is at the same time an application for admission to a PhD programme at the institution. The documentation that is necessary to ensure that the admission requirements are met must be uploaded as an
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materialists, electrical engineers, and computer scientists of TUD, RWTH Aachen and Gesellschaft für Angewandte Mikro- und Optoelektronik mbH (AMO ) in Aachen, Forschungszentrum Jülich (FZJ ), Max Planck
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PhD project (such as Electrical Engineering or Computer Science) is our standard entry, however we place value on prior experience, enthusiasm for research, and the ability to think and work
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model due to the mathematical challenge of solving the multiple partial differential equations simultaneously. With the support of the combined sponsorship from the university and industrial partner
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty