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starting January 2027. Project background While climate change mitigation (e.g., reduction of greenhouse gas emissions) remains important, it is also clear that societies need to adapt to the unavoidable
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networks. Job description The selected candidate will work on the furhter development of spectral submanifold (SSM) methods, aiming to further develop concepts in nonlinear model reduction Possible research
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to further develop concepts in nonlinear model reduction Possible research directions include the development of nonlinear modelling and control approaches for fluid-structure interactions, material with
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for implementing response diversity enabled policy Network: ReDiLEEP - Using science-driven Response Diversity knowledge to Leverage Efficient Ecosystem Preservation and restoration Funding: Marie Skłodowska-Curie
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advanced statistical and computational analyses, including trial-level models, mixed-effects models, clustering or latent-profile analyses, dimensionality reduction, predictive modeling, cross-validation and
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-bundle using our in-house gamma-tomography system AI-assisted reconstruction and imaging of vibrating rods/bundles using sensors and tomography data Use of machine-learning for noise reduction and data
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the electrochemical CO2 reduction reaction (CO2RR) and related electrocatalytic processes. We place a strong emphasis on mechanistic understanding and combine advanced operando characterization techniques, including
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Reduction for Households , or Hydrogen-powered shipping Work independently on assigned tasks in close collaboration with the project team Dive into new technologies and handle analytical, organizational, and