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Field
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motivated Post-Doctoral Associate to join our team with a strong background in robot control, machine learning, and differential geometry to work on the development of advanced algorithms to enhance
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microfluidics channels experimentally. The particle dynamics under solute concentration gradient will be analyzed and new methods of manipulating particles in complex geometries will be developed. Application
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flow within cerebral aneurysms. Arterial geometries are derived from medical scans (e.g., CT) of real patients, which are suitably meshed and processed for numerical treatment using Lattice-Boltzmann
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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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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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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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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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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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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