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of Finland under the supervision of Academy Research Fellow Marcelo Hartmann and Research Fellow Luu Hoang Phuc Hau (Nanyang Technological University) . We have been developing computational algorithms and
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developing computational algorithms and theory grounded in notions of information geometry and Riemannian geometry to enhance Bayesian statistical inference and machine-learning related methods. We are part of
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Responsibilities: The successful applicant will be responsible for: Obtaining theoretical results at the interface of geometry and biophysics Designing, implementing, and testing algorithms to model active matter
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interest in expanding their knowledge in both domains. (1) Geometry/Topology -related methods in computer science. (2) Machine Learning. (For example, graph neural networks, generative networks, or neural
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of quantum experiments and quantum algorithms for computational geometry problems. Prior expertise in these areas is highly encouraged. The selected candidate will work on cutting edge technologies in
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, Quantum Geometry Prof. Moon Jip Park (Hanyang University) – Condensed Matter Physics, Topology, Non-Hermitian Systems, Statistical Physics Prof. Youngseok Kim (Ohio State University) – Quantum Computation
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Application Deadline 12 Oct 2025 - 00:00 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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analysis for more geometries and with a reduced number of sensors - Implementation of the MSE method on a cylindrical structure immersed in water and sensitivity analysis - Algorithmic and experimental
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postdoctoral position is available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research at the intersection of Geometry
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Algorithms. The candidate is expected to conduct research in computer science focusing on the combinatorial aspects of quantum experiments and quantum algorithms for computational geometry problems. Prior