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Field
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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). Access to cutting-edge facilities, including advanced microscopy, controlled environment growth rooms, genomics, proteomics, and metabolomics platforms. Opportunities to work across model and crop species
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extraction, manufacturing, and recycling, are underexplored. This project aims to enhance sustainable machine design through life cycle assessment (LCA), focusing on environmentally friendly materials and
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: Develop innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry
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, AquaCrop) or experience with agricultural or environmental modelling. Some experience with programming in R and/or Python. Exposure to climate or weather data, forecasting systems, or geospatial tools
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This project aims to bridge the gap between technological advancements and their integration into societal and environmental systems by shifting from a product-centric to a service-oriented approach
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of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data
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project “ComDisp: Community-Centered Modeling of Housing-Related Health Disparities.” ComDisp develops a grassroots modeling framework to predict health disparities under different climate change scenarios
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, and environmental sciences, is desirable. • Proficiency in programming languages and software commonly used in modeling (e.g., NetLogo, AnyLogic, Mesa, InsightMaker, etc.) is desirable. • Personal
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Post Doc Associate We are seeking a postdoctoral fellow to lead data integration and quantitative analyses for a project examining connections between human and environmental health around Lake