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monocular depth prediction [7, 8, 9]. A comprehensive benchmarking of these methods will offer valuable insight. Moreover, model-based approaches for 3D reconstruction should also be explored to enable a
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access to the unobserved values, and therefore, cannot compute this error. The goal of this postdoc will be to develop a direct method, based on self- supervised learning. The closest related works are two
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analyzed. The tensor model structure estimated by suitable optimization algorithms, such as that recently developed in [GOU20], will be considered as a starting point. • Exploiting data multimodality and
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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live in. Your role Research related to the following areas: Mathematical statistics, Machine Learning, High-dimensional statistics, Robust estimation methods, Probabilistic foundations of mathematical
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on heuristics, approximation algorithms, and optimization techniques to generate practical solutions that only reach local minima. Some common approaches include: (1) Iterative refinement methods that improve
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PyTorch or TensorFlow is a plus. Useful Information/Bibliography: [1] Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. Federated learning: Challenges, methods, and future directions. IEEE
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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following skills: Strong interest in the field of neuroimaging, psychiatry and genetics. Computer skills: Strong level in the main informatics software (FSL, Freesurfer, fMRIprep) and coding languages (R
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-phenotyping of individuals with autism and controls including brain imaging (EEG and MRI) and a battery of cognitive tests. Our group is currently developing new methods for analyzing whole genome and brain