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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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: ICSA PhD Programme with a start date of 01/09/2025. Applicants should state “Energy Efficient Mobile Networking Systems” and the research supervisor (Prof. Mahesh Marina) in their application and
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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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-class expertise from eight Dutch Universities, five Research Institutes and relevant societal stakeholders that play a major role in research and management of the North Sea. The six-year program (2025
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-supervision of Dr. Claudia Fichtel. The project is part of a broader collaborative effort involving Prof. Peter Kappeler (DPZ), Dr. Ariana Strandburg-Peshkin (Max Planck Institute of Animal Behavior) and Prof
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for Energy Business and Economics Research (CEnBER - https://www.rug.nl/cenber/ ) and in the research programme Economics, Econometrics & Finance of FEB’s Research Institute. The project will be supervised by
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– United Kingdom PhD programme: Harper Adams University PhD programme Fixed term until 30 September 2028 Research project description Agricultural robots and Artificial Intelligence (AI) technologies could soon be
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chemical reaction networks with robotic systems and analytical science. You will also learn how to programme robotic systems and how to implement aspects of deep learning and neural networks for reservoir