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with help from intelligent systems. We are also interested in advanced distributed simulation, software and intelligent systems, and high-performance computing. High-performance computing research
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the physical effects of the propagation environment; computational/numerical modeling using novel and standard approaches, such as, entropy maximization, immunology, and high performance parallel processing; and
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interest is the origin of failure and strength (failure analysis and modes of failure) for current high-performance fiber chemistries and processing. Candidates who are interested in broadening
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are as follows: Elucidate chemical pathways leading to high density, high oxygen-content energetic materials. Synthesize the materials and characterize them using NMR, DSC, IR and small scale sensitivity
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to protecting soldiers from harm. The approach requires the development of intelligent robots that also have a high degree of autonomy. To accomplish this, ARL has developed a robotic control system that has
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performed in collaboration with other computational and experimental teams within ARL and academia. Qualified candidates should be US Citizens and have received their PhD in Materials Science, Mechanical
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background in developing novel analysis techniques from one of the following disciplines: mathematics, statistics, physics, computer science, engineering, biology, psychology and neuroscience. ARL Advisor
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multiple time scales across a large group of individuals, and to use these theories to develop novel methods to predict an individual’s future performance. In contrast to previous efforts that made tradeoffs
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knowledge in database design, and should be undergraduate juniors or above in engineering, computer science, physics or a related discipline. All research will be performed at Adelphi Laboratory Center, 2800
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to discover techniques to enable robust, versatile, closed-loop systems that improve performance throughout the sensory-perceptual-motor decision-making cycle by leveraging knowledge of human nervous system