Dynamical Inference Lab
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About
The Dynamical Inference Lab, led by Dr. Steffen Schneider at Helmholtz Munich, builds robust and interpretable machine learning tools to decompile biological computations. Their work focuses on developing ML algorithms for representation learning and inference of nonlinear system dynamics, applied to complex biological systems in neuroscience, cell biology, and other life sciences. The lab is part of the Institute of Computational Biology at the Computational Health Center and is associated with the Munich Center of Machine Learning and the Graduate School of Systemic Neurosciences. Their research spans computational statistics, various forms of representation learning, and ensuring identifiability and robustness of ML algorithms. They are actively hiring for PhD, postdoc, and research engineering positions, and welcome students for research visits and internships.
Platform
Task
Features
• inference of nonlinear system dynamics
• applications in systems neuroscience and biology
• identifiability and robustness of machine learning algorithms
• hierarchical and compositional representation learning
• computational statistics for dynamical systems
• representation learning (contrastive learning, generative pre-training)
• interpretable machine learning tool development
• robust machine learning tool development
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