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approach that has been developed in the learning sciences over the last 30 years. It implies that researchers and practitioners collaborate in the design, implementation and analysis of the project
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, integration, and analysis of large, diverse datasets that benefit from high-performance computing (HPC). The objective of these fellowships is to facilitate cross-disciplinary, cross-location research through
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for analysis and publication. They will also contribute to the development of new prospective clinical studies, including the establishment of standardized clinical databases and biospecimen collection pipelines
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focusing optics and advanced data analysis methods. The work will include the development, implementation, and testing of experimental instrumentation; performing synchrotron experiments; developing scalable
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and analysis using the EHT. The ideal starting date for BHI postdoctoral fellows is September 1, 2027. Applications should be submitted electronically by 5:00pm EST, Monday, November 16 , 2026, but
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analysis and application of machine learning techniques to structured numerical and unstructured textual data; (d) have a good track record of academic writing, including report writing, manuscript
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methods, data mining, and machine learning methods. Documented shell script, R, Python, and C programming skills. Documented experience in big data analysis and computational tool/package development
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and analysis of soil and vegetation characteristics Participate in drone surveys and process and analyze 3D data Collect and analyze dust samples (MWAC samplers) Maintain automated erosion and
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method development, data analysis, and experimental design. Integrate information from high-throughput screens, proteomic data, and next-generation sequencing data to generate and validate hypotheses
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analysis, instrumentation, or space-mission design and development. Special Instructions Contact Information Nicole Grenier BHI Division Administrator 617-496-8956 Contact Email nicole.grenier