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Dr.-Ing. Joachim Schaeffer

Working area(s)

Interpretable machine learning, lithium-ion batteries, features, Gaussian processes, field data, Bayesian optimization, hybrid modeling

Contact

My research is at the intersection of interpretable machine learning and lithium-ion batteries.

I’m developing robust and interpretable methods to predict cycle life from early test or formation data. Furthermore, I work on the application of Gaussian processes for battery fault detection and prediction from field data.

I care about open source, open data and open science.

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