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quality modelling, with focus on Knowledge-Guided Machine Learning. The position is a rewarding opportunity to be integrated in an excellent freshwater group. The department’s research and advisory
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collaboration and knowledge co-creation and analysing how knowledge is co-created across science, policy, and practice.The postdoc will join the SMARTFIELD project and become part of an interdisciplinary
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collaboration and knowledge co-creation and analysing how knowledge is co-created across science, policy, and practice.The postdoc will join the SMARTFIELD project and become part of an interdisciplinary
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efficiency and smart power converter architecture, and your work will contribute to the development of new knowledge within this field. Your work tasks The energy transition poses fundamental challenges
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research, engineering, and knowledge sharing. Please indicate clearly in your application which position(s) you are applying for. A PhD in chemical engineering, plasma physics, process systems engineering
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knowledge and technologies. Currently, our department has approximately 230 employees, 70 Ph.D.-students enrolled in the Doctoral School in Medicine, Biomedical Science and Technology, and 1,900 students in
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falling, and of decline in cognitive abilities. Together with engineers from the wind turbine company Vestas, interests, the project aims to invent reliable, resilient, and performant solutions
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with a unique problem- and project-based focus, and continued education and knowledge dissemination. The candidate will be supervised by Kim Guldstrand Larsen, Jiri Srba, and Christian Schilling. Your
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machine learning and signal processing, with preferably some knowledge of RF systems or spectrum sensing. You are confident working with experimental RF data such as baseband I/Q signals, developing and
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development of baseline datasets for energy modelling Techno-economic assessment of energy systems and low-carbon solutions Knowledge of energy markets, flexibility services, and system-level value