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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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this initiative, 15 early-stage doctoral candidates (DCs) will be trained through a comprehensive, interdisciplinary program spanning material science, device physics, computer architecture, hardware prototyping
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this initiative, 15 early-stage doctoral candidates (DCs) will be trained through a comprehensive, interdisciplinary program spanning material science, device physics, computer architecture, hardware prototyping
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this initiative, 15 early-stage doctoral candidates (DCs) will be trained through a comprehensive, interdisciplinary program spanning material science, device physics, computer architecture, hardware prototyping
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methodology to better understand the safety and performance risks. Finally, multiscale simulations will be used to map learnings from laboratory-based systems (up to10 kW) to predict the behaviour and
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that are technically well-grounded and at the same time represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster
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therefore teams up materialists, electrical engineers, and computer scientists of TUD, RWTH Aachen and Gesellschaft für Angewandte Mikro- und Optoelektronik mbH ( AMO ) in Aachen, Forschungszentrum Jülich
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computational chemistry and biochemistry studies as directed by Prof. William Goddard within the Materials and Process Simulation Center in the Division of Chemistry and Chemical Engineering Essential Job Duties
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modelling. Application of the produced soft sensor to investigate the feasibility of process control. The project will be supervised by Prof. Jürgen Hubbuch and Dr.-Ing. Iris Perner-Nochta. The successful
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4-year D.Phil. studentship Supervisors: Dr Simone Falco, Prof Daniel Eakins The ability to simulate initiation and detonation effects within energetic materials is a significant capability gap