AI/ML Computational Biologist - Postdoctoral Researcher

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

Offered From the posting source checked Jun 20, 2026

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

U.S. benchmark only; posted salary is not compared across countries or currencies.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

AI/ML Computational Biologist - Postdoctoral Researcher Livermore, CA, United States Company Description: Join us and make YOUR mark on the World! 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. Job Description: We are seeking a Postdoctoral Research Staff Member to conduct research at the intersection of artificial intelligence (AI), machine learning (ML), and computational biology. The successful candidate will apply and adapt state-of-the-art AI/ML approaches to understand host-response dynamics in complex biological systems. Research will focus on integrating large-scale multiomic datasets, including bulk, single-cell, spatial, and multimodal data, from animal models and human cohorts to identify molecular programs associated with disease progression, immune responses, and biological resilience. The candidate will leverage modern machine learning approaches, including deep learning, self-supervised learning, and biological foundation models, to generate biologically meaningful insights from diverse datasets and develop predictive models that generalize across biological systems. This position is in the Integrative Multi-Omics Group and offers the opportunity to work under the guidance of senior scientists on high-dimensional biological data at scale in a collaborative, multidisciplinary environment. This is a two-year term appointment with the possibility of extension to

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