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institution systems may be submitted. Click here for detailed information about acceptable transcripts. A current resume/CV, including academic history, employment history, relevant experiences, and
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the following materials in PDF (preferred) or Plain Text Format: 1. Curriculum Vitae, including full contact information 2. Publication List 3. Research Proposal: Narrative proposal of up to two pages describing
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Policy and Data Analytics Health Professions Education and Interprofessional Learning The ideal candidate will have a strong foundation in research and a deep commitment to cultivating inclusive, student
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, CV, and contact information for three references to: gordon_freeman@dfci.harvard.edu Pay Transparency Statement The hiring range is based on market pay structures, with individual salaries determined
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signaling. Use advanced techniques such as CRISPR/Cas9 gene editing, protein biochemistry, mass spectrometry, and live-cell imaging. Analyze data, interpret results, and present findings at internal and
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units in neural networks, which drive both artificial and natural intelligence. Current projects span a wide range of topics in deep learning theory and theoretical neuroscience. For more information and
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of health care markets. The fellow will also have time for their own complementary research, and we will facilitate access to our data sources in support of their research. The fellow will be supervised by
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of molecular biology and next generation sequencing is highly preferred. 3. Familiarity with single cell data analysis is preferred, but not required. 4. Ability to work independently as well as part of
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, math, statistics, and/or computer science Experience with programming, data science, and geospatial analysis (especially R, Stata, Julia, MATLAB, or Python) An enthusiasm for empirical research and an
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immunotherapy with graph neural networks trained on spatial single-cell tumor microenvironment (TME) data from non-small cell lung cancer (NSCLC). Using high-dimensional datasets, you will learn bi-directional