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formation and energy efficiency. This position targets the core bottleneck: turning complex, sensitive interphases into measurable, interpretable mechanisms through operando and in-situ observation and
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
predictive models for complex multi-physics dynamical systems as well as towards designing observer-based state estimators from output timeseries data measurements. The research also involves development
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
for sequential data modelling, including Physics-informed Machine Learning and Koopman Operator-based representation framework, towards building interpretable predictive models for complex multi-physics dynamical
Searches related to complex manifold
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