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utilize low-grade waste heat. The project aims to develop methods for the design, optimization, and operation of integrated systems that maximize both economic performance and environmental benefits
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. This highly innovative project aims to develop a fully AI driven digital twin that enables real-time optimization and control of fermentation processes. The candidate will develop the digital twin for microbial
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, and optimization. This research is reinforced by close collaborations with industry. You will receive close scientific supervision while having the freedom to shape your research direction, present your
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of materials science, polymer engineering, soft matter physics, and device-oriented prototyping. Information You will: fabricate and optimize liquid crystal polymer coatings with programmable molecular alignment
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Challenge: Extreme water events threaten people and infrastructure Change: Understand impact dynamics with experiments and numerical simulations Impact: Optimize the design of resilient
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of biotechnology relevant compounds. Individual pathway enzymes will be characterized and optimized and fungal strains will be developed that result in optimal production while downstream processing aspects will
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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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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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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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methodologies for evaluating suitable storage sites and optimized lined rock cavern designs; - publish research findings in leading international scientific journals; - present research at national and