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, United States of America [map ] Appl Deadline: (posted 2025/09/04, listed until 2026/02/20) Position Description: Apply Position Description Postdoctoral Associate – Scientific Machine Learning for Multiscale Biological
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, United States of America [map ] Subject Areas: Computer Science Machine Learning Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Appl Deadline: none (posted 2025/08
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, including development of new computational tools for processing large-scale biospecimen data Creation of novel machine learning frameworks for automated scientific analysis and discovery Design and
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alone ––without a deep understanding of Ecology or Evolutionary Biology would in principle not be enough for this position. Fluency in data analysis in R, and strong experimental skills are essential
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for control and analysis of instruments, applying these systems to the study of human diseases, and acquiring and analyzing clinical data sets. Programming skills should include MATLAB, Labview, Python and/or C
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analysis using appropriate machine learning techniques and contribute to the writing of technical papers and research proposals. Duke is an Equal Opportunity Employer committed to providing employment
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Immunology, Data Science and/or related fields. MD/PhD with molecular biology research experience. Must have experience with analyzing omics data. Familiarity or direct experience with analysis of 10x Genomics
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related field prior to the start date. Responsibilities for both positions will include collaborating on the development of research protocols, data collection and analysis, budget and supply management
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Qualifications: A PhD degree in a field related to exposure science, environmental health, or environmental chemistry; experienced in HPLC-MS analysis and other web lab skills; with publications showing analysis
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including but not limited to microbial ecology, biochemistry, genomics, biostatistics, molecular biology, microbiology, evolutionary biology. Familiarity with metagenomics data analysis, microbial