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Field
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and algorithms to analyze complex data from X-ray, electron, and neutron experiments. This role involves research and development in combining advances in Fourier analysis, optimization, machine
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. Integrate various datasets, such as tree species annotations, climate, and topography, into deep learning algorithms. Test deep learning models (Transformers and CNNs) for optimal accuracy using large
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. The position is for one year, renewable subject to satisfactory performance. Successful candidates will conduct research and develop advanced deep learning and computer vision algorithms. Candidates are expected
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, payload, forward and retrieval algorithms, level 0 to level 1 algorithms, etc.); supporting the development of generic building blocks and modules for end-to-end performance simulators, aiming for maximum
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languages; writing and testing logic in hardware description languages; developing and testing signal processing algorithms from concept to implementation; and performing systems integration and testing
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and testing software in high level languages; writing and testing logic in hardware description languages; developing and testing signal processing algorithms from concept to implementation; and
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on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate
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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric
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for fully automated analysis of nuclear cardiology data using novel algorithms and machine learning techniques, and on the development of integrated motion-corrected analysis of positron emission tomography
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; Associated processing, mitigation, retrieval, correction and calibration algorithms for product generation. For remote sensing this includes algorithmic developments relevant to Lvl1 and Lvl2 ground processing