SIML 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.

Qualifications :

• 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.
• Bachelors or Masters 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

$6748 - $7718 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.

Additional Information :

#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!
• Flexible  Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
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  This position requires a Department of Energy (DOE) Q-level clearance. If you are selected we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship. 

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  Remote Work :

  No

  Employment Type :

  Full-time

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