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 senior research scientists with an extensive background in experimental systems neuroscience and excellent quantitative skills. Ideal candidates will have several years of practical experience designing and performing neuro-behavioral and/or neuro-physiological experiments, including visual stimulus design and large-scale electrophysiology techniques (including Neuropixels). 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:•Lead an interdisciplinary team (2-4 team members) dedicated to developing (i) novel stimulus and behavioral paradigms or (ii) large-scale Neuropixels recordings, for experiments in head-fixed behaving animal species beyond current state-of-the-art methods.•Develop and implement new paradigms from hardware to software components, including (i) virtual reality or (ii) probe mounting and registration, ensuring smooth integration of various systems.•Coordinate with other teams of the Enigma Project to ensure efficient, large-scale, industry-level quality of experiments, including the design of relational databases suitable for large data sets.•Manage projects and tasks, and provide regular progress reports to the entire Enigma team.

Key qualifications:•PhD in Systems Neuroscience or related fields.•At least 5 years of practical experience in designing and running neuro-behavioral and/or neuro-physiological experimental paradigms.•Expertise in developing and implementing hardware and software components for such experiments, including stimulation systems (e.g., visual or auditory), eye tracking, electrophysiology, etc.Preferred qualifications:•Experience with designing databases in Python/MySQL for big data.•Experience with engineering solutions for large-scale experiments in neuroscience.•Experience in leadership and supervision.What we offer:•Leadership role in 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.Application: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 $132,000 to $165,000 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.