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properties that are not found in nature. The candidate's role would be to explore new design concepts, simulate their behavior using modeling tools, and collaborate with experimental teams to fabricate and
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Campus in North Austin. Purpose This position aims to advance innovative processing and acquisition methodologies for land DAS and dense surveys, focusing on synthetic modeling, 4D analysis, and seismic
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modeling. These projects will focus on how to leverage ideas from a combination of partial differential equation based computational methodologies and machine learning techniques to develop computational
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skills to develop experimental methods, troubleshoot process issues, and interpret complex data sets. Utilize data analysis software, simulation tools, and relevant programming languages for modeling
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advanced data analysis methods, simulation tools, and scientific programming to support process modeling and predictive materials design. Translate experimental findings into scalable, commercially viable
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numerical calculations to simulate collective behaviors of bacteria. Build theoretical models to describe or explain the experimental or numerical results. Help organize the research lab. Help train other
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of this position is to develop a massively parallel version of a computer code called Commander, and apply this to archival data from Planck HFI, new data from Simons Observatory, and simulated data from LiteBIRD, a
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Previous experiences in simulation-based experiments in networking research Strong mathematics background for optimization and analytical modelling of complex systems Programming languages including C
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distribution systems, EV charging modeling, distributed energy resources, optimization, control, machine learning, hardware-in-the-loop simulation. Expertise in programming languages such as Python, C
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system models remain incapable of simulating this damaging impact of winter warming on ecosystems, and subsequent feedbacks to the atmosphere. Therefore, this PhD position will focus on the physiological