Basic Life Research Scientist

Stanford University School of Medicine; Palo Alto (CA); On-site; USD 78,000 - 90,000; Full time

Job summary

Stanford University School of Medicine's Ophthalmology Department seeks a Research Scientist in Neuroscience to lead investigations using large neural datasets and computational models. You will work with neuroscientists, ML researchers, and engineers on flagship projects, translating data and models into rigorous studies.

The role emphasizes team-based science, cross-disciplinary collaboration, and translating findings into publications and conference presentations.

Qualifications

• Ph.D. in Neuroscience, Computational Neuroscience, Biology, Physics, Computer Science, or a related field.
• Two or more years of research experience beyond the Ph.D. in systems, computational, or experimental neuroscience.
• Deep understanding of visual neuroscience and the relevant primary literature.
• Experience with physiological or behavioral experiments, hands-on or via collaboration.
• Experience analyzing large-scale neural or behavioral datasets.
• Fluency in Python and its scientific stack, and ML methods with PyTorch.
• Strong publication record demonstrating research depth and completion of projects.
• Ability to formulate questions independently and carry projects to publication.
• Enthusiasm for team-based science and collaboration across disciplines.

Responsibilities

• Generate and prioritize scientific questions about neural representation and computation for large-scale data.
• Contribute to a small number of flagship projects as part of an interdisciplinary team.
• Design and run in-silico experiments on brain foundation models to explore model behavior.
• Analyze large-scale neural activity and behavioral data using statistical and ML methods.
• Work with experimental teams to design validation experiments and scalable analyses.
• Define analysis and tooling requirements with engineering teams for large-scale work.
• Collaborate with agentic AI tools to automate analyses and scale hypothesis evaluation.
• Interpret findings in the context of neural computation literature and publish results.

Skills

Python

PyTorch

Machine learning

Education

Ph.D. in Neuroscience, Computational Neuroscience, Biology, Physics, Computer Science, or a related field

Job description

Job Description

The Department of Ophthalmology at the Stanford University School of Medicine advances vision science and eye care through research, education, and clinical excellence. Its research spans the molecular and cellular biology of the visual system, the function of retinal and cortical circuits, and computational and systems neuroscience. The department is committed to rigorous, collaborative science and to fostering an environment where researchers from diverse backgrounds and disciplines can do their best work.

This position is part of the Enigma Project (enigmaproject.ai), a large-scale interdisciplinary initiative based at Stanford University focused on recording and decoding the brain at the highest coverage and resolution technically feasible today. The project is co-directed by Stanford faculty Andreas Tolias, Sophia Sanborn, and Tirin Moore, alongside IMEC Chief Scientist Barun Dutta. Enigma integrates large-scale neural recordings, digital twins, and foundation models of the brain to investigate the principles underlying perception and cognition. These technologies enable systematic in-silico experiments that complement biological experimentation and allow researchers to examine relationships among sensory input, behavior, and neural activity at unprecedented scale. The work aims to deepen our understanding of natural intelligence while identifying principles that may inform the development of artificial systems.

Position Summary

The Enigma Project is seeking a Research Scientist in Neuroscience to lead scientific investigations that leverage large-scale neural datasets and computational models to address fundamental questions about brain function. The successful candidate will work as part of an interdisciplinary team of neuroscientists, machine learning researchers, and engineers focused on a small number of high-impact flagship projects rather than developing an independent research program. The Research Scientist will help identify and prioritize scientifically important questions, evaluate what can be learned from available data and models, and translate those opportunities into rigorous, impactful studies. The role will involve shaping research directions, interpreting complex experimental and computational results, and driving projects from initial hypotheses through analysis, validation, and publication. Dedicated engineering and infrastructure teams will provide the computational pipelines, models, and tools needed to support the scientific work. This position is well suited to a scientist who is excited by the opportunity to pursue neuroscience at unprecedented scale and to work at the intersection of experimental science, computational modeling, and machine learning. Success in the role will require strong scientific judgment, intellectual curiosity, and the ability to collaborate across disciplines to turn complex datasets and emerging technologies into discoveries that advance our understanding of neural computation. The Enigma Project emphasizes team-based scientific discovery. Research questions, contributions, credit, and authorship are shared across project teams and reflect the collective contributions of the scientists, engineers, and researchers involved. This is a two-year fixed-term position, with the possibility of reappointment.

CORE DUTIES

• Generate and prioritize scientific questions about neural representation and computation that can be addressed with large-scale neural recordings and brain foundation models, and develop them into concrete, testable hypotheses.
• Contribute to a small number of shared, high-impact flagship projects as part of a team of neuroscientists, machine learning researchers, and engineers.
• Design and run in-silico experiments on brain foundation models to explore model behavior, characterize neural tuning and population structure, and generate predictions about the biological system.
• Analyze large-scale neural activity and behavioral data using statistical, machine learning, and population-geometry methods, and evaluate model predictions against experimental data.
• Work with the experimental team to identify, prioritize, and design validation experiments, including model-guided and closed-loop paradigms.
• Define the analysis, modeling, and tooling requirements of the scientific program for the engineering team, and work closely with engineers to run analyses at a scale no individual researcher could reach alone.
• Work with agentic AI tools and AI scientists to automate analyses and scale hypothesis generation and evaluation.
• Interpret findings in the context of the wider systems and computational neuroscience literature, and assess their scientific significance and limitations.
• Communicate results through team publications, conference presentations, and internal scientific meetings.
• Contribute to the scientific direction of the project and share scientific expertise across the team.
• Contribute to grant proposals and scientific reporting as appropriate.
• Other duties may also be assigned.

QUALIFICATIONS/MINIMUM REQUIREMENTS

• Ph.D. in Neuroscience, Computational Neuroscience, Biology, Physics, Computer Science, or a related field.
• Two or more years of research experience beyond the Ph.D., for example postdoctoral training, in systems, computational, or experimental neuroscience.
• Deep understanding of visual neuroscience, including the anatomy, physiology, and computational principles of the visual system, and command of the relevant primary literature.
• Direct experience with physiological or behavioral experiments, either hands-on or through sustained close collaboration with the experimentalists generating the data.
• Demonstrated experience analyzing large-scale neural or behavioral datasets, such as two-photon calcium imaging, high-density electrophysiology, or high-throughput behavioral recordings.
• Fluency in Python and its scientific computing ecosystem, and in the machine learning methods used to analyze neural data, including practical experience with a deep learning framework such as PyTorch.
• Strong publication record in neuroscience, demonstrating scientific depth and the ability to carry projects through to completion.
• Demonstrated ability to formulate research questions independently, design analyses or experiments to address them, and carry projects through to publication.
• Enthusiasm for team-based science, and a demonstrated ability to work productively on shared projects with scientists and engineers from other disciplines.
• Excellent written and verbal communication skills.

Preferred

• Research experience with non-human primates.
• Experience working with encoding models or digital twin models of neural activity, and with model-guided or closed-loop experimental design.
• Hands-on experience with in vivo neurophysiology, for example two-photon imaging or Neuropixels recordings.
• Experience working with agentic systems for automating scientific analysis.
• Experience working within large, collaborative, multi-investigator research programs.
• Experience specifying technical requirements to, or working alongside, dedicated engineering or data infrastructure support.
• Contributions to open-source scientific software.

Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Stanford welcomes applications from all who would bring additional dimensions to the University’s research mission.

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.

The expected pay range for this position is $78,000 to $90,000 per annum.