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Postdoctoral Fellow, Kelley Lab - Gene Regulation Machine Learning

Calicolabs

South San Francisco, CA permanent

Posted: February 17, 2026

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Quick Summary

Postdoctoral Fellow, Kelley Lab - Gene Regulation Machine Learning, with a strong background in machine learning and gene regulation, and experience with a variety of programming languages, particularly Python. The role involves working on a cutting-edge project focused on the application of machine learning to understand and regulate gene expression. The ideal candidate will have a Ph.D. in a relevant field and experience with deep learning frameworks such as TensorFlow or PyTorch.

Job Description

Who We Are

Calico (Calico Life Sciences LLC) is an Alphabet-founded research and development company whose mission is to harness advanced technologies and model systems to increase our understanding of the biology that controls human aging. Calico will use that knowledge to devise interventions that enable people to lead longer and healthier lives. Calico’s highly innovative technology labs, its commitment to curiosity-driven discovery science and, with academic and industry partners, its vibrant drug-development pipeline, together create an inspiring and exciting place to catalyze and enable medical breakthroughs.

The Calico Postdoctoral Fellowship Program

We are seeking ambitious early career scientists to join us in our mission to increase our understanding of the biology that controls human aging. This is a unique opportunity for individuals to distinguish themselves in aging research, deepen scientific training, and develop research independence.

The Postdoctoral Fellowship is a fixed-term educational assignment (three years with a potential fourth year extension) with rolling start dates. Postdoctoral Fellows are fully integrated into Calico's labs and community.

The program is designed to train individuals to become independent investigators in an academic or industry setting upon successful completion of the assignment. Fellows will receive regular feedback and guidance from their mentor(s) and participate in a formal annual review process to review project progress and individual performance and provide career coaching.

Responsibilities

• Lead Independent Research: Focus on their own project(s), which should pursue new research directions with dedicated resources for innovative and potentially high-risk/high-reward research

• Contribute to Calico’s Core Mission: Contribute to furthering an understanding of aging and age-related diseases

• Drive Scientific Progress: Execute experiments meticulously, analyze data (including computational analysis if applicable) and troubleshoot procedures as needed

• Publish Work: Generate work that is expected to be submitted for publication in a timely fashion upon completion of the assignment

• Collaborate and Communicate: Actively collaborate across Calico's diverse teams and scientific community, including presenting progress and results to diverse audiences of computational scientists, lab scientists, and engineers

• Professional Development: Engage with a community of other Postdoctoral Fellows and senior leadership through professional and career development opportunities, such as the annual Postdoc Summit, research-in-progress presentations, senior leader lunch sessions, and skill-building workshops

Position Requirements

The successful candidate will demonstrate characteristics valued across Calico's programs, including being a self-motivated team player, detail-oriented, extremely organized, and comfortable working on complex problems.

• Education: Must have recently completed a PhD degree in a relevant scientific or computational field

• Current PhD student applicants should be within 12 months of completing their PhD

• Applicants who have already graduated must have completed their PhD within the past 3 years and not have been employed as a postdoc at more than one other lab/company since graduation

• Experience: Must have made important contributions to science in their area, ideally demonstrated by having had a meaningful first author paper accepted for publication

• Technical Versatility (General): While specific technical skills are project-dependent, proficiency in core scientific methodologies, analytical skills, and quantitative problem-solving is essential

• Communication: Strong teamwork and communication skills are required

• Adaptability: Must be flexible and able to respond quickly to shifting priorities, demonstrating a "can-do" attitude

• Onsite Availability: Must be willing to work onsite five days per week

Application Requirements

The Postdoctoral Fellowship has four application components:

• Research proposal (1-2 page research proposal on what you’d like to investigate in the Kelley Lab during your fellowship - note that project goals may evolve during the training period)

• Updated CV with a full list of publications

• Cover letter

• List of 2-3 references that we may contact, including your PhD thesis advisor and any postdoctoral advisors (if applicable)

The estimated base salary for this role is $95,000. More information on our postdoctoral fellowship program can be found here.

About the Kelley Lab

Our group develops deep learning methods for regulatory genomics. The goal is a comprehensive model of the human cis-regulatory code that predicts how every nucleotide in the genome affects cell-type-specific gene regulation. We apply these models to predict genetic variant effects, amplifying insights from human genetics by boosting statistical power across the allele frequency spectrum, pinpointing causal variants, and generating mechanistic hypotheses.

Calico’s structure means close collaboration with experimental groups and access to rich longitudinal phenotyping across large human cohorts, which lets us connect sequence-level predictions to outcomes that matter for aging. To fully leverage this, we seek highly quantitative, deeply curious, and intellectually courageous collaborative team players who thrive in a multidisciplinary environment. We welcome proposals that address core questions in the application of machine learning to derive insights from genomics, connecting sequence-level predictions to outcomes that matter for aging.

Publications from the group include:

• Žiga Avsec et al., Effective gene expression prediction from sequence by integrating long-range interactions (2021) Nature Methods

• Han Yuan & David R. Kelley, scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks (2022) Nature Methods

• Johannes Linder, Divyanshi Srivastava, Han Yuan, Vikram Agarwal & David R. Kelley, Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) Nature Genetics

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