Dynamical Inference Lab
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About
The Dynamical Inference Lab at Helmholtz Munich develops robust and interpretable machine learning tools for decompiling biological computations. Their research focuses on computational statistics for dynamical systems, representation learning (using contrastive learning and generative pre-training), hierarchical and compositional representation learning, and the identifiability and robustness of machine learning algorithms. They apply these algorithms to model complex biological systems in neuroscience, cell biology, and other life sciences. The lab offers fully-funded PhD, postdoc, and research engineering positions, as well as research visits and internships for undergraduates and Master's students.
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Keywords
Features
• representation learning
• generative pre-training
• contrastive learning
• inference of nonlinear system dynamics
• robust and interpretable machine learning tools
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