SI/ML Computational Biologist - Academic Graduate Appointee

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. You will 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 computer science, data science, bioinformatics, computational biology, statistics, or a related field. Coursework, research, internship, or project experience analyzing high-dimensional biological data (e.g., genomics, 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 and the ability to work effectively in a collaborative research environment. Qualifications We Desire Exposure to or experience applying machine learning or deep learning methods to biological data, including single-cell, spatial, or multimodal omics, and 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 (e.g., Nextflow or Snakemake). Interest in immunology, host-pathogen biology, or disease modeling. Relevant research experience, publications, internships, or substantial technical projects. Pay Range $6,748 - $7,718 Monthly Please note that the pay range information is a general guideline only. Many factors are taken into consideration when setting starting pay including education, experience, the external labor market, and internal equity. #LI-Onsite Position Information This is a one-year Academic Graduate Appointee, open to those who have been awarded a degree at the time of the employment offer. Why Lawrence Livermore National Laboratory? Included in 2026 Best Places to Work by Glassdoor! 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