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and PhD students. Research spans a wide range. Current interests include: Bayesian statistics; modelling of structure, geometry, and shape; statistical machine learning; computational statistics; high
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technological impact in various systems, extending the study to more complex geometries such as 2D structures and highly multi-modal non-Hermitian devices. Recent studies have demonstrated that a system composed
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control, machine learning, and differential geometry to work on the development of advanced algorithms to enhance the safety and robustness of human-robot interaction. The successful applicant will engage
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control. Creating generalizable tools for various battery types, geometries, and chemistries. The scope of methods and applications will be tailored in collaboration with the selected candidate. The work
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particles in complex geometries will be developed. Application relevant materials will be examined such as drug delivery. Publications in multiple high-impact journals are expected. The selected candidate is
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have the opportunity to work on projects such as investigating multicellular organization in curved geometries, exploring the rheology of multilayered epidermal organoids, and designing amorphous
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coil patterns and core geometries, to reduce coil loss in HPMCs You will also contribute to teaching and supervising BSc and MSc student projects, and be co-supervisor for PhD students. Anyone who: has a
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differential geometry, numerical analysis/PDEs, and/or dynamical systems theory. Additional Information: Applicants must complete the Penn State application and must submit an application through Mathjobs.org
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: Dynamical Systems Control Theory Formal Methods Reachability Analysis Computational Geometry Context The applicant will be directly advised by Prof. Matthias Althoff (https://www.ce.cit.tum.de/cps/members
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parts with complex geometries for turbomachinery used in land-based power generation. However, the deployment of L-PBF turbine components is inhibited by an insufficient understanding