Stanford University is launching an interdisciplinary Neuro-AI project dedicated to building a foundation model of the brain. This endeavor 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. Leveraging cutting-edge advances in electrophysiology and machine learning, this project aims to create a functional "digital twin" — a model that captures both the activity dynamics of the brain at cellular resolution and the intelligent behavior it generates, including perception, motor planning, learning, reasoning, and problem-solving.
This ambitious initiative promises to offer 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 Systems Engineers with an extensive background in implementing and maintaining experimental setups used to run large-scale neuroscience experiments. Ideal candidates will have several years of practical experience in building and running multi-modal experimental setups, including implementation of state-machines, the synchronization of different data modalities (such as visual stimulation with eye tracking), and large-scale electrophysiology techniques. Additionally, candidates should exhibit a strong background in quantitative fields such as Mathematics, Physics, Engineering, or Computer Science.
This position promises a vibrant and cooperative atmosphere within the laboratories of Andreas Tolias (https://toliaslab.org), Tirin Moore (https://www.moorelabstanford.com) 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.
Role & Responsibilities:
•Work together with a team dedicated to developing novel stimulus and behavioral paradigms, as well as large-scale Neuropixels recordings, for experiments in head-fixed behaving animal species beyond current state-of-the-art methods.
•Build and maintain multi-modal experimental setups, including integration of hardware components (e.g. eye/face/body cameras, widefield stereo displays, binocular eye trackers, head-stages for electrophysiology, high precision timing, photodiodes and relays) and software components (e.g. LabVIEW, OpenEphys, Kilosort), the latter in collaboration with Software Engineers.
•Work together with other teams of the Enigma Project to ensure efficient, large-scale, industry-level quality of experiments.
Key qualifications:
•PhD, Master’s or Bachelor’s degree in Electrical Engineering, or a related field.
•2-3 years of practical experience in building and running experimental neuro-behavioral and/or neuro-physiological paradigms.
•Expertise in implementing hardware and software components for such experiments including concepts of state-machines, real-time high throughput data acquisition and storage, signal processing, visual stimulus presentation, and high resolution eye tracking
Preferred qualifications:
•Proficiency in LabVIEW, Matlab, and Python.
•Experience with National Instrument hardware.
•Experience with designing databases in Python/MySQL for big data.
•Experience with problem-solving and engineering solutions for large-scale experiments in neuroscience.
•Experience with networking and cloud storage.
What we offer:
•Work on a collaborative and uniquely positioned project spanning several disciplines, from neuroscience to artificial intelligence and engineering.
•Work jointly with a vibrant team of researchers and scientists in a project dedicated to one mission, rooted in academia but inspired by science in industry.
•Competitive salary and benefits.
•Strong mentoring in career development.
Please complete the basic application through Stanford Careers, and we also ask that you send your CV and one page interest statement to: recruiting@enigmaproject.ai
The expected pay range for this position is $102,000 to $122,000 per annum.
Stanford University has provided a pay range representing its good faith estimate of what the university reasonably expects to pay for the position. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the experience and qualifications of the selected candidate including equivalent years since their applicable education, field or discipline; departmental budget availability; internal equity; among other factors.
