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novel statistical methods motivated by medical research needs Solid background in causal inference and survival analysis Experience with clinical trial research, machine learning, and high-dimensional
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medical research needs Solid background in causal inference and survival analysis Experience with clinical trial research, machine learning, and high-dimensional statistics (desirable but not required
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-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. The second project, knowledge in survival analysis and machine learning is desired
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developing and applying advanced statistical models, machine learning, and deep learning approaches. As such, we seek applicants with strong quantitative backgrounds in remote sensing and time series analysis
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many opportunities to learn new and advanced state of the art techniques and strengthen their grant and fellowship application skills. In addition, the candidate will have a broad range of local and
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theory, multi-objective optimization and machine learning. The specific project aims to understand the multiscale interactions shaping human gut bacteria and human gut pathogens. The project will combine
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recent Ph.D. in microbiology, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative
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behavioral assays related to reinforcement learning and neuropsychiatric disease models. • Combine DART with modern imaging and behavioral tools to examine neurobiological mechanisms. • Analyze data, document
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. Ability to execute appropriate computational and statistical analyses and present data in 5. Abililty to learn from existing literature to develop new methods and strategies to analyze complex datesets. 6
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interested in applicants that have experience in one or more of the following areas: satellite remote sensing, energy balance modeling, and machine learning. In addition to scientific expertise, the successful