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Job Description Are you passionate about leveraging IoT, machine learning, and optimization to make energy districts and communities more sustainable? We are looking for a highly motivated and
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industry to a great extent. The position is offered in relation to the research program "Power Electronic Control Reliability and System Optimization" and the postdocs will be positioned to the section
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for the construction industry and enable CO2 capture from the atmosphere. Your objectives will include to: Develop new optimization and/or machine-learning based reconstruction and segmentation algorithms to improve
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, a large initiative funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use of machine learning and remote sensing to site
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program aimed at tackling one of the life science industry’s biggest challenges: Closed-Loop Design and Optimization of Biologics. The research program will build on the recent advances in protein design
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part of the Dynamical Systems Section. We perform research within a broad range of areas within dynamical systems including modeling, optimization, forecasting, and controlling in both deterministic and
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to enhance enzyme stability, recyclability, and catalytic efficiency, including advanced surface modifications, optimized support materials, and improved reactor designs. The Department of Chemical and
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translate fundamental insights in membrane technology into practical, impactful solutions. Responsibilities and qualifications Develop and optimize highly structured membranes for membrane distillation. Lead
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mathematical and analytical models to predict coil loss, facilitating the optimal design of HPMCs Constructing a large-signal platform to measure coil loss of HPMCs Exploring innovative solutions, such as new
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-based topology optimisation and de-homogenisation Adaptive meshing algorithms for topology optimization PDE-driven topology optimisation methods Research fund application Collaboration with industrial