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. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in directed evolution. This position
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systems. This work will specifically focus on combining ML algorithms with classical data analysis and control techniques to develop robust in situ (i.e., in real-time, during the operating experiment
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, economics, and all branches of science. Current concerns include the development and analysis of algorithms for the solution of problems of estimation, simulation and control of complex systems, and their
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materials. We are interested in studying the structure and mechanical properties of polymer networks with defined molecular topologies (functionality, branching, and molecular mass distribution), as
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-eddy simulation and direct numerical simulation of the phenomena. Topics of interest include algorithm development numerical combustion, scientific visualization, and data analysis. key words Buoyancy
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, this project will employ emerging proteomics techniques (such as data-independent acquisition) and will be working alongside software and algorithm developers to ensure that these platforms can be used beyond
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Computational electromagnetics; Integral equations; Numerical algorithms; Fast multipole method; Field solvers; Eligibility Citizenship: Open to U.S. citizens Level: Open to Postdoctoral applicants Stipend Base
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the distribution of drugs in the body and the accumulation of chemicals by organisms in the environment. This approach ignores the difference between storage lipids (triacylglycerides) and membrane lipids
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further enriches the available data from which material behavior can be extracted. Separate work is being done to develop robust algorithms to quantitatively compare the physical and simulated experimental
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also have familiarity with X-ray Diffraction and Pair Distribution Function anaylisis, experience with data acquisition using the Bluesky experiment orchestration package, and knowledge of beamline