96 parallel-computing-numerical-methods Fellowship research jobs at Harvard University
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and Regenerative Biology has an exciting and broad multiomics program focused on brain aging. Approaches will include experimental and computational efforts across multiple labs at Harvard's Faculty
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for Biomedical Imaging (Harvard/MIT/Mass General). In parallel, there will be opportunities to analyze and publish existing data upon identifying areas of mutual interest. The appointment is for one year with a
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. accredited colleges, universities, and U.S. Government laboratories across the country. Participants in the IC Postdoc Program have achieved numerous significant accomplishments, including: More than 450
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The Dean's Postdoctoral Fellowship The Dean's Postdoctoral Fellowship Program is administered through Harvard Medical School Office for Diversity Inclusion and Community Partnership whose mission
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especially encourage candidates with proven experience in applying computational and experimental methods to social scientific questions – including aptitude in working with large-scale datasets and text
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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric
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computational analysis of functional genomics datasets with an emphasis on single-cell omics. This person will develop new computational methods to analyze novel RNAseq, ATACseq, and Spatial Transcriptomics
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(yes, that means some museum and fieldwork!). Comparative analysis using advanced computational tools and wet lab techniques. Hands-on dissections of invertebrates for anatomical and physiological
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interpersonal and communication skills. While not a must, a strong background in computational methods and/or statistical methods is a plus. Special Instructions Applicants should submit a formal application and
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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric