SI/ML Computational Biologist - Academic Graduate Appointee

LLNL•Livermore, CA

7d•Onsite

About The Position

Lawrence Livermore National Laboratory is hiring an Academic Graduate Appointee for interdisciplinary research at the intersection of super intelligence, machine learning, and computational biology. The appointee will support processing, integration, and analysis of 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 investigating molecular programs related to disease progression, immune responses, and biological resilience. The position is in the Integrative Multi-Omics Group of the Biosciences and Biotechnology Division. It is a one-year Academic Graduate Appointee appointment, with the possibility of extension to a maximum of two years, and is open to candidates who have been awarded a degree by the time of the employment offer. The role provides opportunities to develop technical skills and professional experience under the guidance of experienced staff. Full-time on-site presence is required due to the nature of the work.

Requirements

Ability to secure and maintain a U.S. Department of Energy Q-level security clearance; this requires U.S. citizenship and a federal background investigation.
Bachelor’s or Master’s degree in computer science, data science, bioinformatics, computational biology, statistics, or a related field.
Coursework, research, internship, or project experience analyzing high-dimensional biological data, such as genomics or transcriptomics, using standard bioinformatics pipelines and statistical methods.
Foundation in machine learning and statistical modeling demonstrated through coursework, research, or technical projects.
Proficiency in Python and/or R.
Proficiency in Linux/Unix, including command-line tools and shell scripting.
Strong written and verbal communication skills.
Ability to work effectively in a collaborative research environment.

Nice To Haves

Exposure to or experience applying machine-learning or deep-learning methods to biological data, including single-cell, spatial, or multimodal omics.
Experience running large-scale workflows on GPU or high-performance computing (HPC) systems.
Familiarity with multiomic data integration, transfer learning, or foundation models for biological data.
Experience with version control (Git) and workflow managers such as Nextflow or Snakemake.
Interest in immunology, host-pathogen biology, or disease modeling.
Relevant research experience, publications, internships, or substantial technical projects.

Responsibilities

Collaborate with experimental scientists, computational biologists, computer scientists, and engineers on defined computational and data-analysis assignments under senior staff guidance.
Process and analyze bulk, single-cell, and spatial transcriptomic data from animal models and human cohorts using established bioinformatics methods.
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.
Support analysis linking molecular states to disease trajectories and host-response phenotypes.
Support 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.