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of flexible energy resources (e.g., heat pumps); knowledge of mathematical modelling, optimization, machine learning, or data analytics (experience in one or more is desirable); strong scientific programming
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the bearing. To optimally re-lubricate, it is vital to understand this mixing process. Currently, the scientific knowledge of re-lubrication is non-existent. This project will build new knowledge on several key
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. Model transition pathways for the ABEL technologies to integrate into existing or new value chains. Minimize energy and resource use through efficient heat exchange, waste heat recovery, and optimized
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use through efficient heat exchange, waste heat recovery, and optimized process conditions. Assess and reduce environmental impacts, such as greenhouse gas emissions. Validate models with literature and
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substantive research pillars and a work package on knowledge transfer. This vacancy concerns a PhD position within Pillar 2. You will collaborate closely with the PhD candidates in the other pillars and will be
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with advanced photoreactors, modular LED light sources, inline analytics, and machine-learning algorithms for the autonomous optimization of photocatalytic reactions. Research will address key challenges
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describing the electron optics of the projection column and optimize its performance through large-scale simulations. You will use commercial electron-optical simulation software but also develop custom Python
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, and high-tech companies covering the full energy value chain. Your main focus will be on modeling systems essential for the design and optimization of hydrogen production processes. Accurate
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elements should be used to generate the required radiation? how do we generate as many ‘usable’ photons from the plasma as possible? What are the optimal laser and target properties? Project goal The goal
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necessary to get a better grip on these interactions, but also to make sure that uncertainties are propagated in a sound and robust way. The envisaged PhD candidate shall investigate optimal ways