SI​/ML Computational Biologist - Academic Graduate Appointee

• Full-time
• Organization:
  Physical and Life Sciences
• Category:
  Physical Life Sciences
• Employee Referral Bonus:
  Not applicable
• Job Code 1: 710.0 Academic Graduate Appointee Ex
• Pre-Employment Drug Test:
  Required for external applicant(s) selected for this position (includes testing for use of marijuana)
• Pre-Placement Medical Exam:
  Not applicable
• Position Type:
  Academic Graduate Appointee
• Security Clearance:
  Anticipated DOE Q clearance (requires U.S. citizenship and a federal background investigation)

Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.

Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.

Note: This is a one-year Academic Graduate Appointee with the possibility of extension to a maximum of two years.

We have an opening for an Academic Graduate Appointee to support interdisciplinary research at the intersection of super intelligence, machine learning, and computational biology. The appointee will assist with processing, integrating, and analyzing large-scale multiomic datasets, including bulk, single-cell, and spatial transcriptomic data from animal models and human cohorts. Using established bioinformatics methods and, under guidance, machine-learning approaches such as deep learning, self-supervised learning, and biological foundation models, the appointee will contribute to analytical and predictive workflows to identify molecular programs associated with disease progression, immune responses, and biological resilience, and support the generation of biological insights.

The position offers opportunities to develop technical skills and professional experience under the guidance of experienced staff.

This position is in the Integrative Multi‑Omics Group of the Biosciences and Biotechnology Division.

This position requires full-time on-site presence due to the nature of the work.

• Collaborate with experimental scientists, computational biologists, computer scientists, and engineers on defined computational and data‑analysis assignments under guidance from senior staff.
• Process and analyze bulk transcriptomic, single‑cell and spatial transcriptomic data from animal models and human cohorts using established bioinformatics methods, and assist with workflows for multimodal data integration.
• Assist with implementing, testing, and applying established machine‑learning methods, including interpretable models, for prediction and representation learning from high‑dimensional biological data, under guidance, linking molecular states to disease trajectories and host‑response phenotypes.
• Support the training, adaptation, and evaluation of self‑supervised and foundation models for biological applications.
• Document code and analyses to support reproducibility.
• Contribute to technical reports, presentations, and potentially peer‑reviewed publications.
• Present research progress and technical results to internal teams, collaborators, and other scientific audiences with guidance from senior staff.
• Meet project deadlines and maintain high‑quality deliverables.
• Perform other duties as assigned.

• Ability to secure and maintain a U.S. Department of Energy Q‑level security clearance, which requires U.S. citizenship and a federal background investigation.
• Bachelor’s or Master’s degree in…