34 programming-"https:"-"FEMTO-ST"-"UCL"-"https:"-"https:"-"https:"-"J" positions at NIST
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, constraint programming, Bayesian methods, sparse kernel machines, graphical models, and deep learning. Some examples of materials classes of interest for this project are photovoltaic, thermoelectric
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parameter space challenging due to the sheer number of possible compositions. To enable rational design of these materials, we have developed a highly adaptable sample environment that can be programmed
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301.975.6662 Description An experimental and modeling program is underway to further the understanding of dynamic processes that occur in fires and to reduce the impact of fire on people, property, and the
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learning, the Group creates living measurement systems, such as cells, engineered to sense and respond in programmed ways. Importantly, building these systems both requires and advances meaningful