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Field
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. Performance assessment, Evaluate generated designs using key performance indicators such as energy efficiency, safety, cost, and emissions to identify optimal vessel configurations. Research Environment You
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. Performance assessment, Evaluate generated designs using key performance indicators such as energy efficiency, safety, cost, and emissions to identify optimal vessel configurations. Research Environment You
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manufacturing and optimization of advanced PTL/MPL architectures, with the goal of improving efficiency, durability, and reducing precious-metal requirements. This is a unique opportunity to combine modeling
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in the field of hybrid dynamical systems, observer design, learning techniques, and optimization. Knowledge of at least one programming language: Matlab, Python, is expected. Eager to work within a
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that can self-learn bulk visco-elastic properties? How to structure such materials to learn continually and counteract the aging of their own parts? Can we optimize self-learning materials to achieve
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through optimization of ion channels incorporation and activity in lipid bilayers. The project sits at the interface of biophysics, engineering and biochemistry. The PhD student will be part of
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, including EEG. You will design and optimize experimental tasks, collect and analyze behavioral and neural data, and adapt computational models to capture variability between individuals. What you will be
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Wetsus - European centre of excellence for sustainable water technology | Netherlands | about 2 months ago
Research Infrastructure? No Offer Description Topic background A proper functioning drinking water distribution network is of major importance for society and keeping this network optimally operational is
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Wetsus - European centre of excellence for sustainable water technology | Netherlands | about 2 months ago
ED configurations for hybrid desalination schemes without the need for added chemicals. Your work will combine ED stack design with membrane modification strategies to optimize ion selectivity and
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to the timing of events and the results from performance optimization need to be included in the models. This PhD position will address these challenges by developing new timing-aware distributed supervisory