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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
new statistical and machine learning methodology. Areas of focus include (but are not limited to): * Clustering and unsupervised learning * Dimension reduction and manifold learning * High-dimensional
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to work in an interdisciplinary environment. Desirable Skills: Experience working with or supporting a scientific facility/instrument platform. Knowledge of graph-based methods, manifold learning
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
reduction and manifold learning * High-dimensional inference and feature selection * Generative modeling and digital twins * Reliability and interpretability of ML methods The postdoctoral researcher will be
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, Lausanne 1015, Switzerland [map ] Subject Areas: • stochastic differential equations (SDEs); stochastic partial differential equations (SPDEs); stochastic processes on manifolds; multi-scale stochastic
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, -- stochastic analysis, -- mathematical theory for artificial intelligence, -- optimization and numerical computation over manifold, -- systems and control theory, -- algebraic computation theory and cryptography
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, simplicial complexes, manifold theory, fiber bundles, curvature). Familiarity with topological data analysis—persistent homology, filtrations, stability theorems—is particularly valued. Extensive experience
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dedicated to a manifold range of research and academic teaching. Your future tasks: Active participation in research, teaching & administration, which means: You build up an independent research profile in
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part of the vibrant PostDoc community at Boehringer Ingelheim in Biberach with manifold opportunities for scientific, cross-functional exchanges for your personal development. You will have the
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Do you want to contribute to groundbreaking research in the development of a theoretical framework and numerical algorithms for evolving stochastic manifolds? This is an exciting opportunity for a
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to mathematics Naomi Feldheim Probability and analysis Gaussian processes, random functions, rare events, harmonic analysis Shira Faigenbaum-Golovin Manifold learning, shape space analysis, machine learning