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type, developmental stage, treatment) to build tissue- and context-specific co-regulation networks Design and implement clustering and integration approaches (e.g., network-based and subspace clustering
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This KTP aims to position Jesmonite as a market leader in sustainable composite materials. The project's vision is to embed environmental responsibility at the core of Jesmonite's product innovation
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Position Overview School / Campus / College: School of Medicine Organization: Rehabilitation Medicine Title: Research Assistant Professor, Center for Limb Loss and Mobility (CLiMB) at VA Puget Sound
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University of California, San Francisco | San Francisco, California | United States | about 2 months ago
collaboration with data analysts and other collaborators on research design and presentations; performing literature searches (e.g., advanced PubMed) and synthesizing results concisely; assisting with
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of the University of Minnesota. NRRI is guided by the forward-looking charter provided by the Legislature to foster the economic development of Minnesota’s natural resources in an environmentally sound manner
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-tracking experiments; troubleshoot equipment and software Manage and analyze data, including LENA audio data Coordinate with collaborators, contractors, and app developers Contribute to conference
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for no longer than one (1) year. This title does not provide a career track and carries limited benefits. • (60%) Plan, design, conduct, and/or assist with research. • (30%) Analyze data and write reports • (10
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modelling and the land-surface model used in the project. Develop simplified, fast-running model surrogates using machine-learning methods to replace very time-intensive simulations. Design an efficient
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Design and implement clustering and integration approaches (e.g., network-based and subspace clustering) Use co-regulation networks for gene function and protein–protein functional relationship prediction
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model surrogates using machine-learning methods to replace very time-intensive simulations. Design an efficient training strategy for these machine-learning tools, making use of existing model simulations