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Electrical Engineering (or equivalent), have a solid mathematical background (e.g. in control theory and optimization) and have taken specialized courses in at least one of the following disciplines: advanced
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). Background in modeling of dynamical systems and control theory. Knowledge of fluid dynamics and related physical modeling. Strong programming skills (e.g. Python, MATLAB, or similar) for data analysis and
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systems and control theory. Knowledge of fluid dynamics and related physical modeling. Strong programming skills (e.g. Python, MATLAB, or similar) for data analysis and model development. Ability to work
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catalytic metals. These clusters will be deposited with high control over size and composition using cluster beam deposition on morphologically engineered TiO2 nanotubes. The most efficient catalysts will be
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-metallic clusters that combine plasmonic and catalytic metals These clusters will be deposited with high control over size and composition using cluster beam deposition on morphologically engineered TiO2
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the controlled flow at tunable temperature and photopolymerization of the precursor. The practical work will be complemented by fluid mechanics computer simulations, including solutions employing machine learning
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-metallic clusters that combine plasmonic and catalytic metals These clusters will be deposited with high control over size and composition using cluster beam deposition on morphologically engineered TiO2
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accessibility and gene expression data for all major cell types forming the peripheral nerve environment (as well as controls), generated from the patient-derived iPSCs and integrate these data into the cis
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chromatin organisation, chromatin accessibility and gene expression data for all major cell types forming the peripheral nerve environment (as well as controls), generated from the patient-derived iPSCs and