Daniel Lang is a postdoctoral researcher at the Institute of Machine Learning in Biomedical Imaging at Helmholtz Munich. Additionally, he holds the role of a principal investigator in the Helmholtz Imaging Project CLARITY, and is a fellow of the Helmholtz HPP postdoc program.
His research primarily focuses on the development of machine learning models for dynamic imaging, with a specific emphasis on applications in cancer diagnosis and treatment. Daniel’s key areas of interest include but are not limited to: generative modeling, synthetic image generation, self-supervised learning, transfer-learning, and domain adaptation.
- dynamic imaging
- generative modeling
- self-supervised learning
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PhD in Physics, 2022
Helmholtz Munich and Technical University Munich
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MSc in Physics, 2018
University Regensburg
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BSc in Physics, 2016
University Regensburg