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incentives, network constraints, and regulatory decisions in shaping collective outcomes. Identifying optimal designs and interventions is further complicated by multiple, often competing, objectives
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retrieve shapes, overlay errors, and other geometrical parameters of the target using methods ranging from local and global optimizers to priors and neural networks developed by partners in the project. Job
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to increase resource use efficiency. However, managing and optimizing these new cultivation systems requires crop models that can predict plant growth, crop yield and resource use under highly dynamic
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catalyst structure–performance relationships. The most promising catalysts will then be optimized and evaluated under practically relevant electrolysis conditions, contributing to the development of a
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brings together expertise in energy informatics, optimization, data science, social sciences, and governance to develop trusted data-sharing solutions for the heat transition. Within the project, you will
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and prior knowledge of the sample geometry. You investigate and optimize the trade-off between accuracy and imaging speed. For real-time application, the speed and accuracy of the algorithms will be
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operate. The complexity and size of indoor optical systems pose challenges in scalability, power consumption, and maintenance. While wireless networks present a viable solution for indoor environments by
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. The state-of-the-art approaches in optimal climate control of greenhouses are based on implementing economic objective functions exploiting a time scale decomposition between short-term climate control/energy
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at cryogenic conditions: Many advanced technologies — like quantum computers, powerful microscopes, and chip-making tools — require extreme cooling. However, the optimal design of cooling systems at cryogenic
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Utrecht, CWI, Dutch municipalities, DSOs, and other societal/industry partners. The project brings together expertise in energy informatics, optimization, data science, social sciences, and governance