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the required laboratory tools Applying parameter estimation and optimisation methods, supported by sensitivity and identifiability analyses Parameterising and validating battery models using test-cell
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based on these models. Performing parameter estimation and uncertainty quantification using Bayesian inference techniques based on experimental and/or synthetic data. Investigating efficient data
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parameter estimation theory Programming (e.g., in Matlab, Python, Julia, or similar) In addition to fulfilling the technical requirements, you are thorough in your work and have a critical mindset
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infrastructure behind our digital twins. You will: develop and benchmark methods for parameter estimation and uncertainty quantification, from classical optimisation and Bayesian inference to physics
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regions. Job description The research focuses on investigating/developing generic methods and algorithms in the context of multi-modal synthetic aperture radar (SAR) for the estimation of environmental
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range of topics including atmospheric deep convection, extra-tropical cyclones, heat waves, ensemble prediction and algorithmic parameter estimation techniques as well as future changes to our weather
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work with computationally intensive analysis routines Evidence of skills in image or bio-signal (e.g. ECG) analysis Evidence of skills in model personalisation: parameter estimation, uncertainty