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learning for healthcare within two primary areas: 1) Precision phenotyping of complex, heterogeneous conditions and, 2) Safe and Effective Deployment of Machine Learning in the clinic. Job Summary We
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research group develops statistical and machine-learning tools for high-dimensional and spatial data, with a particular emphasis on applications in plant microbiology and microbial ecology. The group
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research experience. Preferred Competencies Background conducting quantitative research in healthcare. Proficiency in optimization, statistics, machine learning, and econometrics. Outstanding verbal and
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. The appointee will primarily conduct research applying advanced machine learning/AI (including techniques like deep learning) to analyze complex biological and clinical data (e.g., single-cell multi-omics
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: Demonstrated skills in learning new data science-related techniques. Previous experience working in a data science area. Technical Skills or Knowledge: Knowledge and experience with data science basics (Python
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to these challenges, working with high performance and distributed computing environments, working with large-scale machine learning models, and a proven research record of scholarly contributions through publications
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September 2025. The position requires a PhD in History or a related field and demonstrated commitment to undergraduate teaching. The successful candidate will teach a total of three courses per year
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field. Experience: At least three years of strong record of research productivity in machine learning and artificial intelligence. Expertise in AI/ML and interests in business and policy applications
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, developmental biological, molecular genetic and/or genomic experiments in a basic research laboratory and will learn to design, execute and interpret experiments with increasing independence. Other
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of the four campuses (two in Chicago, one in London, and one in Hong Kong) reflects the architectural traditions of its environs while offering a state-of-the-art learning environment. Chicago Booth is proud