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Field
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large-scale surveys in the field, experiments, survey design, and strong skills in applied micro-econometrics and causal inference. An understanding and/or prior involvement with the CGIAR system and
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coarse-grained models that can be analyzed and simulated. Strong applicants with backgrounds in applied and computational mathematics, biophysics, engineering, statistical inference, and related fields
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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of analyzing large-scale population data. Experiences working with electronic health records (desirable). Understanding of clinical informatics approaches (e.g., machine learning, Bayesian statistics) and
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topics ranging across programming language (especially Bayesian statistical probabilistic programming), statistical machine learning, generative AI, and AI Safety. Key Responsibilities: Manage own academic
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analyses, including demographic inference, selection scans, and gene-environment and gene-phenotype association studies. • Plan and conduct fieldwork to collect plant material across Arctic locations, and
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algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology, and expertise in computational methods, data analysis, software
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one of the following: Econometric methods for causal inference; Data science and machine learning; Survey design and analysis; Qualitative analysis skills specialized in policy and geopolitics A good
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to evaluate and validate naïve pluripotency. Transcriptomics and network inference will be supported by computational/bioinformatics members of the team. Chimaera studies will be in partnership with our
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algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology, and expertise in computational methods, data analysis, software