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Field
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coatings e.g. oxide and nitride. Drive the comprehensive characterization of thin films using advanced techniques (e.g., SEM, XRD, Raman, AFM, nanoindentation), enabling deep understanding of structure
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group selection management to promote structural complexity, making them a useful test of different modeling approaches in the Forest Vegetation Simulator (FVS). Currently FVS users, which largely consist
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fellow for a research project titled, “Structural modification and update of the U.S. national harvested wood products carbon model (WOODCARB II)”. USDA Forest Service has been using the WOODCARB II model
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the last 5 years or soon to be completed. Demonstrated experience in building machine learning/deep learning models using one or more large scientific data sets involving sequence, protein structures
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deployment. Existing methods rely on fixed data and static models, which struggle to adapt to real-time changes and unpredictable conditions. This limits the ability to optimize energy storage use for critical
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either Digital Culture, Comparative Literature or Nordic Literature. The AI STORIES project is a European Research Council Advanced Grant led by Professor Jill Walker Rettberg, with a team of three
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management expert and a research assistant. AI STORIES explores the hypothesis that deep narrative structures in the datasets used to train generative AI models are replicated and perhaps exaggerated in
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devices in appropriate benchtop models Analyze patient data for trends in bifurcation structures Disseminate research results through authorship of journal articles, conference papers, and invention
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possibility of further appointment] Duties The appointees will assist the project leader in the research project - “Advancing large recommendation models: Techniques for high-quality data construction and
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(CNTFET) for ternary computing. Semiconducting CNTs are ideal for transistor channels due to their material properties and 1-D structure. With the continuous need of improving transistor performance, CNTFET