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Field
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intermediates and correlate surface coverage with reaction kinetics. Develop advanced characterization workflows - establish robust methodologies for sampling, handling, and analyzing catalysts from long-term
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relationships between powder composition, microstructure and instant functionality, ultimately providing new strategies for designing dairy powders with faster, more reliable and more robust reconstitution
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University Hospital (Klinikum rechts der Isar) and works on privacy-preserving machine learning for healthcare, with a focus on bridging the gap between empirical privacy methods and provably robust solutions
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global guarantees of stability and robustness to disturbances, uncertainties and actuation limits (saturation); b) the analysis of the stability and performance properties of the proposed solutions; c
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bio-metrology, the project will validate a robust and repeatable production route for industrial-scale micro-carrier fabrication. Key objectives (and acquired competences): Define and characterise
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data • Design clinically meaningful benchmarks and robust evaluations • Publish at leading machine learning and medical AI venues • Collaborate with clinicians, computer scientists, and European partners
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addresses the "calibration problem" in particulate continuum models and particle simulations. Specifically, it focuses on developing robust methodologies for selecting and parameterising contact models, a
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systems. This may involve introducing mechanical constraints, such as restoring forces, to the system. The PhD student will investigate the minimal set of components that are needed to generate robust
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and purely data-driven methods, which often lack robustness, interpretability, and reliability in uncertain and noisy conditions. To overcome these challenges, the PhD will explore multimodal sensor
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the robustness of software systems, and in responding to sophisticated cybersecurity threats. Ensuring the robustness and security of AI systems is the next big challenge on the horizon, and we are seeking life