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Field
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captures neural activity and intelligent behavior at unprecedented scale and resolution. This ambitious project spans multiple institutes including the Wu Tsai Neurosciences Institute, Stanford Bio-X, and
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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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humans in playing board and computer games, driving cars, recognizing images, reading and comprehension. It is probably fair to say that an artificial neural network can perform better than a human in any
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prediction of gene perturbation effects for drug discovery. The successful candidate will play a leading role in developing gene perturbation models that combine foundation models (FMs) and graph neural
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academic institutions. Individual will develop and test novel computational models of the neural activity generated by electrical stimulation of the brain. Also, perform data analysis utilizing medical
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disorder. This project investigates early neural markers of psychosis by integrating multimodal neuroimaging with genetic and transcriptomic data and applying machine-learning approaches to identify
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electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in
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, neural differentiation, and bioinformatic analysis while addressing fundamental questions about how chromosomal imbalances cause developmental disorders. This work has direct translational potential, as
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pathology. The successful candidate will gain expertise in stem cell biology, genome engineering, neural differentiation, and bioinformatic analysis while addressing fundamental questions about how
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Fellow applicants interested in investigating the neural basis of natural behavior in amphibians. Current funded projects in the lab include building neuroscience tools for amphibians and studying