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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
Web
Task
system inference

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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