Postdoctoral AI Researcher in AI​/ML Cellular and Protein Computational Biology

School:
Faculty of Arts and Sciences Position Description

The Kempner Institute at Harvard University seeks early‑career researchers to help shape the future of AI as Postdoctoral AI Researchers. We are looking for candidates with deep expertise in modern AI/ML and a strong record of research accomplishment who are excited to develop new AI approaches for high-impact problems in cellular and protein computational biology.

This role focuses on applying modern AI/ML methods to protein and cellular biology, including protein structure prediction, protein‑protein and small‑molecule‑protein docking, cell state prediction from large‑scale Perturb‑seq datasets, and multimodal modeling of protein function and cellular state.

We seek candidates with strong technical preparation in modern AI/ML, a demonstrated record of scholarly achievement, and experience in computational biology or biological data analysis. Strong candidates may come from protein‑focused, cell‑state‑focused, or multimodal biological modeling backgrounds and will have expertise in one or more of the following areas:

• Foundation model training, evaluation, and adaptation
• Protein structure modeling and docking
• Cell state prediction from large‑scale perturbation datasets
• Multimodal modeling for protein function and cellular state prediction
• Familiarity with Alpha Fold, RF Diffusion, Cell Cap, or related models for protein and cellular biology

Postdoctoral AI Researchers will work closely with Kempner faculty, researchers, and students on foundational machine learning and biologically informed scientific applications. The position is particularly well‑suited to candidates eager to apply their technical expertise in modern AI to important problems in protein biology, cellular systems, 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.

• Postdoctoral AI Researchers conduct research under the general supervision of one or more Harvard 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.

• PhD in computer science, statistics, electrical engineering, applied mathematics, computational biology, bioengineering, biophysics, 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, RECOMB, ISMB, 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 biology, biological data analysis, protein modeling, cellular modeling, or related areas.
• Ability to work effectively in a collaborative research environment and communicate technical work clearly.

• Experience with foundation model training, post‑training, adaptation, or evaluation.
• Experience with protein structure modeling, protein‑protein docking, or small‑molecule‑protein docking.
• Experience with cell state modeling from large‑scale…