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Field
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of nonlinear PDEs or the development of new solver- or coupling-methods incl. their convergence analysis, but also modeling and simulation aspects across a wide range of fields - from biomechanics and geophysics
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guiding new mathematical and algorithmic developments. A Ph.D. in mathematics, physics, computer science, or a closely related area is required. To be considered for the initial review, with an intended
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the development of efficient algorithms and codes for multilinear algebra, with a particular focus on the use of innovative parallel programming models and tools. In the context of this task and as part of the Exa
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on developing algorithms to analyse optical signals extracted from video images to obtain vital signs with high accuracy. Key responsibilities include: Robust feature extraction from biomedical signals
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genomic graphs. The project will also deliver efficient algorithms to train these models under budget and time constraints, facilitating flexible adoption of the methods. The project is carried out in close
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to algorithms with actionable performance guarantees. More specifically, the research will revolve around the following theme: High probability convergence in stochastic optimization under heavy-tailed noise
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of control software and data management systems for the automated laboratory. Development and deployment of AI algorithms for adaptive experimental planning, optimization of experimental space, and automated
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. Key responsibilities include: Data Analysis: Assist in collecting and analyzing student performance data to inform the recommendation engine. Algorithm Development: Contribute to designing, prototyping
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setups and develop methods for in-situ diagnostic and monitoring additive manufacturing (AM) processes toward the 3D printing of metal alloys and ceramics. The successful candidate will use optical and
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the “Inverse Problem” component of the project: developing and validating a satellite-based pancake-ice detection algorithm using ICESat-2 wave-damping observations within a Bayesian inversion framework