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of the parameter space in relation to the statistical model. One of the main goals of SLT is to quantify the complexity of such models w.r.t. the data generating process (and some prior probability distribution
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
Dependent on Experience/Qualifications Proposed Start Date Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching, research and
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that combine first-principles multiphase flow descriptions with data-driven components Formulate and implement parameter estimation and system identification methods for multiphase flow models Integrate
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quite new within the field of computer vision. The neuromorphic design allows for a much higher acquisition frequency but most and foremost much longer acquisition time spans. This makes it ideal
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collaboration with local water utilities and software developers Integrate digital urban water twins with data, applying methodologies for data assimilation, parameter estimation, and quantification of model
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manufacturing process, such as: Identify any key parameters in the mfg process that impact material performance Optimise power/energy usage throughout the process The outcome will be to optimise the manufacturing
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) for dynamic systems with unknown but measurable performance functions. Experience in system identification and online time-varying parameter estimation algorithms. Programming skills in MATLAB/Simulink, C/C
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improved performance in tasks of systems analysis like parameter estimation, solving inverse problems, and uncertainty quantification. The successful candidate will join a multi-institution research team
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parameter space, and using and/or developing agent-based models for the movement and behavior of fish in rivers. Presenting material at conferences, writing research papers for publication, and/or assisting
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+ 0.05 PT + 0.05 PST&DSC, where the abbreviations STW, PE, SS&TA, PT, PST&DSC are the classifications of the indicated parameters. The classification STW corresponds to the following ratings: 19 to 20