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structure modeling, protein–protein docking, or small-molecule–protein docking Experience with cell state modeling from large-scale perturbation datasets, including Perturb-seq or related data Experience with
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) Location: Big Rapids, MI 49307 Department: College of Engineering Technology Advertised Salary: $13.73-$13.99 Summary of Position: The Clerical General Level I position will consist of office work and will
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and maintaining accurate student records, dealing with large data sets, providing administrative support to key University groups and supporting large scale events. This post is fixed term for 12 months
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 hours ago
related field with a strong background in machine learning, statistical modeling, or geospatial data analysis; experience with deep learning methods for spatiotemporal data; experience working with large
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large-scale mapping and modelling of agricultural data, machine learning to track environmental change, collecting and working with large and complex datasets. Before applying, please note that to work
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organization from large affinity matrices. By combining quantitative interaction data with molecular interpretation, the project aims to bridge network topology and mechanistic understanding. Importantly
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lead and support advanced statistical analyses of trial and large-scale administrative datasets, including Medicare and Medicaid claims, electronic medical records (EMR), and/or other health-related data
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animal disease modeling, including small and large animal platforms. The Research Specialist will contribute to the design, execution and optimization of preclinical research projects within
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Summer 2026 Math Research Assistant (Student Position) Apply now Job no: 497690 Work type: Student Employment - General Location: Big Rapids, MI 49307 Categories: Student Employment - General
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. The role requires developing and applying custom code for data processing, statistical analysis, and modeling of large-scale neural datasets. This work aims to uncover fundamental principles governing