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: Develop and validate physics-based, data-driven, or hybrid digital twins of Electrolysis systems, capturing their electrochemical, thermal, flow, and system-level behaviour for system monitoring, state
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BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and
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heat integration in integrated Power-to-X systems. You will develop and apply physics-based process models that describe the behavior of Power-to-X plants under varying operating conditions. This
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qualification listed above. We are happy to support a strong ML candidate in growing into the embedded/hardware aspects of the project. Your competencies You are able to design and conduct independent research