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The Machine Learning (ML) group led by Professor Ricardo Henao at KAUST (King Abdullah University of Science and Technology) has multiple openings for Postdoctoral level positions to conduct
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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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are looking for young scientists who are passionate about water desalination and machine learning with its potentially transformative applications in research and industry. The two-year post-doctoral position
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Postdoctoral Researchers to work on research problems at the interface between a selection of the following areas: - Bayesian inference for deep learning - Generative modeling - Computational statistics
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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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their key clinical phenotypes under various conditions. We then employ computational methods, in particular machine learning methods, to integrate the phenome and genome data with the goal
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their key clinical phenotypes under various conditions. We then employ computational methods, in particular machine learning methods, to integrate the phenome and genome data with the goal
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of state-of-the-art bioinformatics pipelines; (ii) single-cell analysis, multi-omic analysis; (iii) spatial transcriptomics bioinformatic analysis. Learning and implementing Systems Medicine-based analysis