339 molecular-modeling-or-molecular-dynamic-simulation positions at The University of Chicago
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sources. The job performs data analysis assignments related to data manipulation, statistical applications, programming, analysis and modeling in order to support the research efforts of faculty members
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, epidemiology, or another quantitative field is required. Projects include (a) modeling the longitudinal dynamics of individual immune responses after infection and vaccination, (b) examining variability in
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cultivating global partnerships. This role requires a dynamic leader to ensure operational excellence and amplify the global impact of the research portfolio. Responsibilities Leads the design, planning, and
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an award recommendation. Establishes strategic relationships with the supply base; conducts research on market dynamics, cost drivers, supplier business models, and historical spend to drive cost savings
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Summary The principal goals of the ISAC DRC are the provision of robust consultation in research design, data modelling, and database applications; surveying and monitoring of emerging tools and
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datasets and to advance research in regulatory genomics, sequence-to-function modeling, and disease genetics. Responsibilities Plan, execute, and facilitate advanced technical/scientific projects; design
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, a person in this position will be a key contributor to the design and implementation of algorithms, AI/ML models, and workflows to enable the discovery of valuable information in large volumes of data
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). Oversee daily operations of the Student Activities Center, including budgeting, inventory management, space maintenance, service delivery, and copy charge administration; supervise, train, and mentor a team
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copy and seeks blurbs and endorsements for books. Works with authors and client publishers on developing and executing marketing plans for their book, including researching and contacting media and
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internal data systems as well as from external sources. Designs and evaluates statistical models and reproducible data processing pipelines using expertise of best practices in machine learning and