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Field
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on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine/deep learning with GWAS/sequencing data and other types of omic
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Qualifications: PhD in experimental particle physics at the time of appointment. Preferred: Deep understanding of the particle detectors, particle identification, data analysis Machine learning experience is a
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and future climate scenarios—including counterfactuals—and integrating them with health surveillance and related data to estimate climate-attributable risk under Deep Uncertainty. The candidate will
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Pharmaceutical health outcomes, (Pharmaco)epidemiology, Biostatistics, or a related field. · Expertise in or a strong interest in machine learning and deep learning algorithms. · Excellent communication skills
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A PhD in immunology or related field with a deep understanding of mucosal immunology is required. Expertise working with primary human cells, intestinal tissues/organoids, murine models of intestinal
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engineering, neuroscience, computational biology, or a closely related field ï‚· Strong oral and written communication skills ï‚· Demonstrated motivation, initiative, and attention to detail ï‚· Deep interest
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projects utilizing machine learning, deep learning, and generative AI to solve business and healthcare problems have been undertaken at the Insight Lab. For more details, please refer to: https
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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute
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is available in the exciting field of mathematics of deep learning, under the joint supervision of Prof. Alex Cloninger and Prof. Gal Mishne at UC San Diego. This NSF-funded research focuses on a
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with health surveillance and related data to estimate climate-attributable risk under Deep Uncertainty. The candidate will also contribute to the development of interactive tools and training materials