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wildland-urban interfaces— across a wide range of climate conditions. Using machine learning methods, we will optimize the weightings of each contributing factor and identify the key drivers of wildfire risk
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PhD in a relevant field such as structural mechanics, heat transfer, numerical optimization, topology optimization, or lattice design. The postdoctoral scholar will be responsible for vigorously
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production using state-of-the-art data science methods, leading to improved research in forestry and economics. Additionally, the fellow will be invited to participate in the broader research goals using
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including measurements of electrical conductivity, enzyme kinetics, pH, unconfined compressive strength, etc. Share the findings of the research via a formal technical presentation Where will I be located
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activities including measurements of electrical conductivity, enzyme kinetics, pH, unconfined compressive strength, etc. Share the findings of the research via a formal technical presentation Where will I be
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surveys and other anecdotal collection methods Combine empirical evidence gathered from web/social media analytics Study data associated with content Collaborate with other team members to create multimedia
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conditions they are likely to be useful. For this reason, we have assembled a research team to explore new methods and new data that will improve foundational fuel structure and flammability information
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will have the opportunity to expand your knowledge by collaborating on the following activities: Applying existing software tools to model, analyze, and design microgrids for designated applications
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in a laboratory General computer skills to include making presentations and analyzing data in spreadsheets or statistical/graphing software Application Requirements A complete application consists of
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. Along the way, you will engage in activities and research in several areas. These include, but are not limited to: Use software, coding and analysis as you learn about curating data for machine learning