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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
, seeks a Post-Doctoral Associate or a Research Associate to join a lab focused on applied machine learning. The successful applicant will participate in research involving human computation, knowledge
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evolution. ● Experience with population genetics. ● Experience developing R packages. ● Experience applying or developing machine learning tools. ● Strong scientific writing skills. Brief
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analysis. ● Prior work on kinase or other signaling-protein conformational dynamics, phosphorylation-driven activation, or allosteric regulation. ● Familiarity with machine learning and deep learning
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avenues of research rather than a required work plan. They may be pursued individually or in combination, and we welcome other creative and strategic approaches. Statistical or machine-learning approaches
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to the development of remote sensing algorithms, simulation and modeling capabilities, and artificial intelligence/machine learning (AI/ML) methods in support of the goals of the joint NASA/USGS Landsat mission
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or quantitative methods. We are open to a range of methodological backgrounds, including digital trace data analysis, natural language processing, machine learning, experimental design, causal inference, and
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. Applications are welcome from scholars in relevant fields in the humanities and social sciences. The successful candidate will pursue an independent research agenda; teach one undergraduate course in each year
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across learning sciences, computer science, machine learning, HCI and education research. Research Role Research themes for the NTO Postdoctoral Associate include, but are not limited to: Developing
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team, as well as the willingness to learn new methods and techniques and to collaborate with others as the projects evolve. Pay Range: $63,480.00 - $71,000.00. Salary will be commensurate with
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and/or case-crossover analyses, and have a promising publication record. Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization