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control and grid-forming/grid-following operation; HVDC, MTDC and offshore systems; protection; machine and load modelling; model order reduction; parameter estimation and model validation; large-scale
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, uncertainty-aware parameter estimation, surrogate modeling, and reduced-order modeling where relevant. The scientific emphasis is mechanics-first: we are looking for someone with strong foundations in nonlinear
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control probe positioning and ultrasound imaging parameters. 4, Design and test intuitive human-machine interfaces to support non-specialist users in operating wearable ultrasound devices. Conduct
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process modeling, simulation, and systems analysis Experience with experimental work in chemical or process engineering (desirable) Knowledge of process optimization, parameter estimation, or control
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experimental work in chemical or process engineering (desirable) Knowledge of process optimization, parameter estimation, or control methods is a plus Programming experience in MATLAB and/or Python Familiarity
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for parameter estimation, degradation prediction, and analysis of electrochemical and structural characterization data, including X-ray CT image reconstruction, segmentation and quantitative microstructure
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 2 months ago
application to gas hydrate system to develop efficient key parameter estimation tools and large-scale 3D geologic model for gas hydrate reservoir. Learning opportunities will be given on the area of laboratory
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 5 hours ago
migration in porous media under in situ conditions, and • Machine learning application to gas hydrate system to develop efficient key parameter estimation tools and large-scale 3D geologic model for gas
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 5 hours ago
, increased storage) Apply geostatistical methods (e.g., kriging) to characterize the spatial heterogeneity of key reservoir parameters (e.g., porosity, permeability) and quantify subsurface uncertainty through
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Neural Nets (PiNNs) or “ML inspired” traffic models. PhD2 focuses on data assimilation and estimating start and boundary conditions such as path-flows, and other key parameters and inputs. In your role as