Fellow in AI​/ML Cellular and Protein Computational Biology

Title Fellow in AI/ML for Cellular and Protein Computational Biology

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 AI as Kempner AI Fellows. We are looking for candidates with strong foundations in modern AI/ML and the ambition 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 research accomplishment, 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

AI Fellows will work closely with one another and 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.

• 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.

• Bachelor’s or master’s degree in computer science, statistics, electrical engineering, applied mathematics, computational biology, bioengineering, biophysics, 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, RECOMB , ISMB , 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
• 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 perturbation datasets, including Perturb‑seq or related data
• Experience with multimodal modeling for protein…