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on studying shape parametrization, learning gait optimization functions for mechanism design and using different machine learning embeddings (such as GANS, VAEs, and Diffusion Models) for developing a new full
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, and policy. Required Qualifications • PhD in economics, applied economics, or a related field combining computational / quantitative modeling and methods with economic theory and / or applications by
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primate, rodent medicine, surgery, model development, and laboratory diagnostics. For this position, we are seeking an applicant with an interest in laboratory animal medicine in a large, private, research
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demonstration early childhood education site for Keene State College students, practicing professionals, and young children and their families. The Center strives to model best practices by offering nurturing
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based on institutional priorities, space standards, and utilization benchmarks. · Provide data-driven recommendations for space planning, including scenario modeling and cost-benefit analysis. · Develop
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and/or in the motivation letter; Knowledge in mathematical modelling of biochemical systems, namely using systems of differential equations, logical/boolean models and genome scale metabolic models
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tracking tools (e.g., spreadsheets, Google Sheets). Support the check-in/check-out process for residents and guests. Provide campus tours and assist with guest/resident access issues. Create and distribute
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Carolina 27695, United States of America [map ] Subject Areas: Data-driven Modeling, Computational Biology Appl Deadline: 2025/10/01 11:59PM (posted 2025/08/14) Position Description: Apply Position
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Job Title Assistant Research Scientist - Hydrologic and Agroecosystem Modeling Agency Texas A&M Agrilife Research Department Temple Proposed Minimum Salary Commensurate Job Location Temple, Texas
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 18 days ago
for Risk Management and Insurance Innovation (IRMII). This research involves a unique set of skills and expertise to be used in developing computational models that characterize and manage financial risk