Fellow in AI/ML for NeuroAI and Computational Neurobiology

Harvard University; Harvard (IL); On-site; USD 55,000 - 60,000; Full time

Job summary

The Kempner Institute at Harvard University seeks early-career researchers to help shape the future of NeuroAI as Kempner AI Fellows. We are looking for candidates with strong foundations in modern machine learning and the ambition to build brain foundation models and other AI systems that advance our understanding of neural activity, brain circuits, and biologically grounded intelligence.

Fellows will work closely with Kempner faculty, researchers, and students on foundational ML and

Qualifications

• Bachelor’s or master’s degree in a quantitative field required by the expected start date.
• Strong technical background in modern AI/ML with deep learning and frameworks such as PyTorch or JAX.
• Demonstrated research productivity with publications in ICML/ICLR/NeurIPS or similar, or substantial OSS contributions.
• Experience implementing, training, evaluating, or fine-tuning modern ML models.
• Strong Python programming skills and ability to maintain research code.

Responsibilities

• Fellows conduct research under the direction of a Kempner Institute investigator.
• Develop brain foundation models to predict neural activity from large-scale recordings.
• Collaborate with Kempner faculty, researchers, and students on ML and neuroscience applications.
• Contribute to foundational AI/ML methodology and neuroscience-informed scientific work.

Skills

Python

PyTorch

JAX

Research productivity

Collaborative work

Education

Bachelor's or Master's degree in a quantitative field

Tools

Python frameworks (PyTorch, JAX)

Job description

Fellow in AI/ML for NeuroAI and Computational Neurobiology

Title Fellow in AI/ML for NeuroAI and Computational Neurobiology

School

School Faculty of Arts and Sciences

Department/Area

Department/Area Kempner Institute for the Study of Natural and Artificial Intelligence

Position Description

The Kempner Institute at Harvard University seeks early-career researchers to help shape the future of NeuroAI as Kempner AI Fellows. We are looking for candidates with strong foundations in modern machine learning and the ambition to build brain foundation models and other AI systems that advance our understanding of neural activity, brain circuits, and biologically grounded intelligence.

We seek candidates with strong technical preparation in modern AI/ML, a demonstrated record of research accomplishment, and an interest in contributing to ambitious research at the intersection of machine learning, neuroscience, and computational biology. This role centers on computational neurobiology and the use of modern AI/ML methods to model brain circuits and neural activity. In particular, fellows will help develop brain foundation models that predict patterns of neural activity from large-scale, multi-regional recordings.

We are especially interested in candidates with experience in computational neurobiology or neural data analysis, and expertise in one or more of the following areas:

• foundation model training, evaluation, and adaptation
• time-series modeling
• transformers, autoencoders, and dynamical systems models
• modeling brain circuits and neural activity from large-scale recordings
• large-scale scientific applications of AI/ML, including in the life sciences

AI Fellows will work closely with one another and with Kempner faculty, researchers, and students on foundational machine learning and neuroscience-informed scientific applications. The position is particularly well-suited to candidates eager to apply their technical expertise in foundation models to important questions in neuroscience and biological intelligence.

Appointment Terms

• Fellows will conduct research under the direction of a Kempner Institute investigator.
• Fellows are appointed for a one-year term; reappointment may be possible for up to three consecutive years.
• Due to the importance of in-person mentoring, this position is based on campus, full-time, at Harvard University. Remote work for this position is not possible.

Basic Qualifications

• Bachelor’s or master’s degree in computer science, statistics, electrical engineering, applied mathematics, computational neuroscience, computational biology, neurobiology, physics, or a related quantitative field required by the expected start date
• Strong technical background in modern AI/ML, including deep learning and hands-on experience with frameworks such as PyTorch or JAX
• Demonstrated research productivity, including publications in venues such as ICML , ICLR , NeurIPS, COSYNE , CCN , or similar, and/or substantial open-source research contributions
• Demonstrated experience implementing, training, evaluating, or fine-tuning modern machine learning models
• Strong programming skills in Python and experience building and maintaining research code
• Demonstrated ability to use modern AI-assisted and agentic coding tools effectively, such as Claude Code, Codex, or similar systems, in research and development workflows
• Experience in computational neurobiology, neural data analysis, or modeling neural activity from large-scale recordings
• Ability to work effectively in a collaborative research environment and communicate technical work clearly

Additional Qualifications

• Experience with foundation model training, post-training, adaptation, or evaluation
• Experience with time-series modeling
• Experience with transformers, autoencoders, dynamical systems models, or related approaches for sequential or neural data
• Experience modeling brain circuits and neural activity from large-scale, multi-regional recordings
• Experience with large-scale datasets, distributed training, or high-performance computing environments
• Expertise in neuroscience, biologically grounded intelligence, and scientific applications of AI/ML

Special Instructions

• CV.
• A research statement of no more than 2 pages describing your experience using modern AI/ML to model neural activity, brain circuits, or other neuroscience data. Please be specific about your individual contributions.
• References – 1-2 required
  • Please give the emails of up to 2 individuals who can describe your work and your potential for future discoveries.
  • Referees will be contacted to submit the letters directly to the Kempner Institute.
  • The application will not be considered complete until all letters have been received.
• Transcripts (Undergraduate and/or Master’s)

We anticipate a start date of September 15, 2026.

Contact Information

Molly Marshall

Contact Email

Salary Range

Expected annual salary is $54,600 for candidates holding a bachelor’s degree and $60,060 for candidates holding a master’s degree. Salary will be commensurate with qualifications and experience.

Minimum Number of References Required

Minimum Number of References Required 1

Maximum Number of References Allowed

Maximum Number of References Allowed 2

EEO/Non-Discrimination Commitment Statement

Harvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes.

Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university’s non-discrimination policy . Harvard’s equal employment opportunity policy and non-discrimination policy help all community members participate fully in work and campus life free from harassment and discrimination.