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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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investigation is periodic precipitation, where controlled diffusion and reaction kinetics give rise to regularly spaced bands of precipitate, known as Liesegang rings, within porous matrices. These systems will
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water/loose rock matrices such as fine gravels and coarse sands. In a first step, it will be a matter of studying and optimizing the vectorization as well as the ranges of action and influence of chemical
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-agent AI, cooperative vision, and compression protocols so fleets of intelligent machines can perceive the world—robustly, efficiently, and in a trustworthy manner—even when individual sensors fail
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. You have experience in matrix algorithms, data compression, parallel computing, optimization of advanced applications on parallel and distributed systems. An excellent scientific track record proven
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focus lies on a complementary approach of compression testing and high-pressure torsion deformation. The experiments are performed on hydrogen pre-charged, nanostructured metals and require analysis
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applications for a post-doctoral fellowship specializing in magnetic/electromagnetic separation in solid and liquid matrices. Applicants should have a thorough understanding of the principles of magnetic
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, biologists, and data scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and
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Post-Doctoral Associate in the Center for Translational Medical Devices (CENTMED) - Dr. Panče Naumov
, compression molding, injection molding, and fused deposition modeling. This position requires the preparation of ISO/ASTM test specimens for mechanical (tensile and flexural) testing of polymers and composites
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between objects. A common way to represent a graph is to use the adjacency matrix associated with the graph. However, adjacency matrices only model networks with one kind of objects or relations between