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Field
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force fields (MLFFs) that combine state-of-the-art equivariant neural network architectures with robust, well-calibrated uncertainty estimates. These models will enable fully automated active learning in
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fundamental questions about the transcriptional regulation of inhibitory neuronal development and function in neural circuits, the role of cerebellar circuit dysfunction, and disrupted gene regulatory networks
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that methodological advances are developed with direct translational and scalability considerations. Responsabilities: Lead the development of hybrid foundation model-graph neural network architectures for gene
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neurophysiological experiments - mathematical analysis of the dynamics of neural networks - programming and numerical simulations of neural networks - development of quantitative model predictions and
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual
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and implement Bayesian graph neural networks and convolutional neural networks as surrogates for high-fidelity biomechanical models Quantify and propagate uncertainty, and develop strategies for model
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research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware
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aspects of machine learning focusing on efficiency, generalization, and sparse neural networks. Currently we are expanding our expertise by applying our theoretical findings also to robotics. Hybrid is our
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candidate will work with open available datasets obtained in rodents and unique datasets of neural activity. Your primary focus will be to design new learning frameworks and neural network architectures
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weights in a crossbar matrix of synapses based on Mott insulators and participate in neural network inference tests. - Participate in the supervision of the fabrication of the Mott insulator-based synapse