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areas Bayesian inference for deep learning Generative modeling Computational statistics, statistical learning, and deep learning Applications in environmental and life sciences Here are a few recent
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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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learning, deep learning, and AI) for analysis and prediction of genotypic variation Methodology (machine learning, deep learning, and AI) for analysis and prediction based on medical or biological imaging
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Bioinformatics (generative protein design) Methodology (machine learning, deep learning, and AI) for analysis and prediction of genotypic variation Methodology (machine learning, deep learning, and AI
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Bioinformatics (generative protein design) Methodology (machine learning, deep learning, and AI) for analysis and prediction of genotypic variation Methodology (machine learning, deep learning, and AI
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, analytical and reactor microfluidics Organs on a chip for diagnostics and drug discovery Methodology (machine learning, deep learning, and AI) for the analysis, biomarker discovery, and predictive models based
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, Bayesian deep learning, biostatistics, environmental statistics, extreme statistics, geospatial statistics and health surveillance, spatio-temporal statistics and data science, and stochastic processes and
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analysis (e.g., paper-based droplet systems) Nanopore transport Digital/droplet, analytical and reactor microfluidics Organs on a chip for diagnostics and drug discovery Methodology (machine learning, deep
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analysis (e.g., paper-based droplet systems) Nanopore transport Digital/droplet, analytical and reactor microfluidics Organs on a chip for diagnostics and drug discovery Methodology (machine learning, deep
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The VCC center at KAUST is looking for research scientists in Prof. Wonka's research group. The topics of research are computer vision, computer graphics, and deep learning. A suitable candidate