Postdoc: AI/ML Biologist for Multi-Omics Insights

LLNL; Livermore (CA); On-site; USD 105,000 - 142,000; Full time

Benefits offered by this job

Flexible schedules

Job summary

Lawrence Livermore National Laboratory (LLNL) seeks a Postdoctoral Research Staff Member to advance AI/ML methods in computational biology. The role focuses on integrating bulk, single‑cell, spatial, and multimodal omics data from animal and human cohorts to uncover molecular programs of disease progression and immune responses.

You will leverage deep learning and foundation models to generate insights and develop predictive models that generalize across systems, within the Integrative

Qualifications

• PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, Data Science, or related field.
• Strong background in ML, statistical modeling and quantitative biology.
• Experience analyzing high‑dimensional data (genomics, transcriptomics, etc.).
• Proficiency in Python and R.
• Experience with ML frameworks such as PyTorch, TensorFlow, or similar.
• Familiarity with Linux/Unix and scientific computing workflows.
• Demonstrated ability to conduct high‑quality research and publish results.

Responsibilities

• Develop and apply ML methods for high‑dimensional biological data.
• Design and implement workflows for integrative multiomic analyses.
• Investigate multimodal data fusion and cross‑dataset transfer learning.
• Train and evaluate self‑supervised/foundation models for omics data.
• Develop interpretable models linking molecular states to disease trajectories.
• Process and analyze large sequencing and omics datasets.
• Present findings at seminars and conferences.
• Publish results in peer‑reviewed journals.

Skills

Python

R

ML frameworks

Linux/Unix

Research & publishing

Collaborative work

Communication

Statistical modeling

Computational biology

Education

PhD in Computational Biology

Job description

Lawrence Livermore National Laboratory (LLNL) seeks a Postdoctoral Research Staff Member to advance AI/ML methods in computational biology. The role focuses on integrating bulk, single‑cell, spatial, and multimodal omics data from animal and human cohorts to uncover molecular programs of disease progression and immune responses.

You will leverage deep learning and foundation models to generate insights and develop predictive models that generalize across systems, within the Integrative