Stanford University - Stanford, CA
posted 2 months ago
Stanford University is launching an interdisciplinary Neuro-AI project dedicated to building a foundation model of the brain. This ambitious initiative will involve multiple labs and faculty across the Stanford campus, including the Wu Tsai Neurosciences Institute, Stanford Bio-X, and the Human-Centered Artificial Intelligence Institute. The project aims to leverage cutting-edge advances in electrophysiology and machine learning to create a functional 'digital twin' of the brain, capturing both the activity dynamics at cellular resolution and the intelligent behavior it generates, such as perception, motor planning, learning, reasoning, and problem-solving. This endeavor promises to provide unprecedented insights into the brain's algorithms of perception and cognition while serving as a key resource for aligning artificial intelligence models with human-like neural representations. As part of this project, we are seeking talented data engineers with extensive experience in data infrastructure engineering. The team will be responsible for designing, building, and operating the data pipeline infrastructure, which encompasses the entire flow of data from neurophysiological data acquisition to storage, processing, and preparation for large-scale training of foundation models. Ideal candidates will have practical experience in designing and scaling big data pipelines, with proficiency in big-data storage architectures (data lakehouse) and relevant software tools and frameworks including but not limited to Delta Lake, Apache Spark, Apache Parquet, and Apache AirFlow. This position promises a vibrant and cooperative atmosphere within the laboratories of Andreas Tolias, Tirin Moore, and other labs at Stanford University renowned for their expertise in perception, cognition, pioneering neural recording techniques, computational neuroscience, machine learning, and Neuro-AI research.