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Field
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Responsibilities The PDA will conduct research to design and develop optical wireless communication systems. This involves the development of mathematical models for signal transmission/reception, derivation
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be associated with the new national AI LEARN Centre at SLATE. The position is for a fixed term of four years, with the inclusion of career development work (e.g., teaching, project work) accounting
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transport for inverse problems One of the central topics of the research projects is the further development of theory and methods for the concept of optimal transport for inverse problems. Optimal transport
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classical topics in numerical analysis, such as the analysis of nonlinear PDEs or the development of new solver- or coupling-methods including their convergence analysis, but also modeling and simulation
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of the AI algorithms. Key duties Develop a robust framework to simulate streamflow decomposed into fast-flow and baseflow at multiple Moroccan watersheds. The candidate would have to test various fast-flow
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project is to develop scalable and privacy-preserving Bayesian computational algorithms. The position is intended for two to three years, with an initial one-year appointment renewable contingent upon
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The Energy Systems and Infrastructure Assessment (ESIA) division at Argonne provides the rationale for decision makers to improve energy efficiency. ESIA develops and uses analytic tools to help
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Max Planck Institute for Radio Astronomy, Bonn | Bonn, Nordrhein Westfalen | Germany | about 1 month ago
the acceleration of relativistic plasma in jets. Developments of new automated algorithms for VLBI model-fitting, kinematics measurements and robustness assessment. 2. Probing the physical mechanism of neutrino
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Martin and Professor Daniel Paulusma from Durham University and Professor Vadim Lozin from the University of Warwick. The general aim of the project is to develop algorithmic meta-classifications, which
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understanding of non-stationary complex systems through theoretical analysis and numerical simulation develop efficient statistical algorithms for analyzing and inferring dynamical models from multivariate time