Postdoctoral AI Researcher in AI/ML for NeuroAI and Computational Neurobiology

School: Faculty of Arts and Sciences

Department/Area: Kempner Institute at Harvard University

Position Description

The Kempner Institute at Harvard University seeks early-career researchers to help shape the future of NeuroAI as Postdoctoral AI Researchers. We are looking for candidates with deep expertise in modern machine learning and a strong record of research accomplishment who are excited 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 scholarly achievement, 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, Postdoctoral AI Researchers may help develop brain foundation models that predict patterns of neural activity from large-scale, multi-regional recordings.

Areas of particular interest include:

• 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

Postdoctoral AI Researchers will work closely 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 and modern AI/ML to important questions in neuroscience and biological intelligence, while continuing to grow as scholars within a collaborative academic environment.

Candidates should be within 2 years of receiving their doctoral degree and will work under the direction of Kempner Institute faculty.

Appointment Terms

• Postdoctoral AI Researchers conduct research under the general supervision of one or more Kempner faculty members
• The appointment is for one year; reappointment may be possible for up to a total of three years, contingent on funding, project needs, satisfactory performance, and mutual interest.
• This is a full-time, benefits-eligible postdoctoral appointment based at the Kempner Institute at Harvard University.
• Due to the importance of in-person mentoring and collaboration, this position is based on campus, full-time, at Harvard University. Remote work for this position is not possible.

Basic Qualifications

• PhD 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.
• Candidates must have received their PhD on or after September 15, 2024, or be on track to complete all PhD requirements by the expected start date of October 15, 2026.
• Demonstrated expertise in modern AI/ML, including deep learning and hands-on experience with frameworks such as PyTorch or JAX.
• Strong publication record in leading venues such as ICML, ICLR, NeurIPS, COSYNE, CCN, or comparable conferences and journals, 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

• Expertise in neuroscience, biologically grounded intelligence, and scientific applications of AI/ML.
• 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.
• 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.
• Interest in alternative architectures and systems-level approaches to AI

Special Instructions

Please submit the following items in PDF format no later than 11:59pm EST Monday, June 8, 2026:

• 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 - 2-3 required
  • Please give the emails of up to 3 individuals who can describe your previous related work.
  • 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.

Candidates selected for further consideration will be asked to submit a short video presentation reviewing their past work; additional details will be provided at that stage. Following review of the videos, a subset of candidates will be invited to interview with members of the selection committee via Zoom.

Applications received after the deadline will be reviewed on a rolling basis if positions remain available.

We anticipate a start date of October 15, 2026 .

Contact Information

Moly Marshall

Contact Email: [email protected]

Salary Range

Expected salary is $100,000 , subject to compliance with the applicable salary requirements for the appointment. This is a benefits eligible position.

Minimum Number of References Required: 2

Maximum Number of References Allowed: 3