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on the opportunity to which you have applied, as well as a secure link to submit a recommendation for you for this application. If you ask the same person to submit a recommendation for you for multiple applications
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to integrate multiple data sources through machine learning and regression modeling to predict post-fire landscape attributes from field-based and remotely sensed vegetation and fuel structure metrics
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fluorescence, as well as in DNA sequencing techniques. Opportunities exist in studying the relations between wind trajectories and other atmospheric parameters on the types of aerosols measured, and in studying
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developed at ARL for single-camera imaging pyrometry. This research opportunity aligns with the ARL’s core competency of Ballistics Sciences focused on gaining a greater understanding and discovery
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development; direct fire and indirect fire munition flight dynamics; experimental ballistics experience with design and execution of various in-situ diagnostic devices. This research opportunity aligns with
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in theory, modeling, as well as their applications to predicting behavior. Our goal is to extend theories regarding human behavior, incorporating multiple factors and their interactions, spanning
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characterization of individual aerosol particles by using multiple optical methodologies. Preference will be given to candidates having good experience in optics and laser spectroscopy, instrumentation, and
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models at multiple scales are usually developed and combined into a single multi-scale/multi-physics model. With this process, analysis and optimization are possible. Computational analysis and design of
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the development of image/signal processing algorithms from a multidisciplinary approach, to include multiple sensor modalities. These multidisciplinary research opportunities incorporate theoretical and