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world are facing dramatic upheaval as a result of rapid technological change driven simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic
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also include applicant’s commitment to mentorship and contributing to an effective learning environment) Optional Documents: Relevant publications (up to 3) authored/co-authored by applicant Slide deck
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knowledge suitable for processing raw data for analysis (e.g., text manipulation); modern methods in machine learning, AI, including use of LLMs. One or more computational environments for statistical
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large-scale genomic analyses is a new research interest. Theory of Differential Privacy. The selected candidate will be expected to lead research in privacy-preserving data analysis/machine learning
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experience and/or applied experience. Methodological interests in econometrics, statistics, machine learning, industrial organization, productivity, and economic theory are preferred. Must be able to take
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statistical and machine learning methods to address population health disparities in nutrition and cardiovascular disease epidemiology. Areas of interest include: statistical methods for high-dimensional
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network engineering and angiogenesis 3). Applications of machine learning in cell and tissue engineering Candidates should have demonstrated publication records in cardiac and vascular engineering or
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Postdoctoral Research Fellow/Research Associate Position in Biostatistics and Biomedical Informatics
well as large scale genomic measurements. We seek an individual with strong statistical and computing backgrounds and who has expertise in statistical and machine learning methods for big data. The work will
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management, cloud computing, machine learning, and algorithms for the Internet. Example topics of interest include but are not limited to the design and analysis of sketches and filters for use in real systems
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for analysis (e.g., text manipulation); One or more computational environments for statistical analysis (e.g., MATLAB, R, or Stata); Creating and managing very large datasets; Machine learning skills. Basic