Postdoc Scientist: SI/ML & Multi-Omics Biology

Lawrence Livermore National Laboratory; Livermore (CA); On-site; USD 111,000 - 135,000; Full time

Benefits offered by this job

Relocation Assistance

Education Reimbursement Program

Flexible schedules

401(k)

Job summary

Lawrence Livermore National Laboratory seeks a Postdoctoral Research Staff Member to conduct research at the intersection of super intelligence, machine learning, and computational biology. You will integrate multiomic data to identify molecular programs linked to disease progression and host-response phenotypes.

Based in LLNL's Integrative Multi-Omics Group, you will work with senior scientists on high-dimensional data at scale, train state-of-the-art models, and publish in peer-reviewed

Qualifications

• PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, Data Science, or a related field.
• Strong background in machine learning, statistical modeling, computational biology, or a related quantitative discipline.
• Experience analyzing high-dimensional biological data such as genomics, transcriptomics, or related modalities.
• 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 in peer-reviewed journals.
• Demonstrated ability to work effectively in a collaborative research environment.
• Strong written and verbal communication skills.

Responsibilities

• Develop and apply machine learning methods for prediction and representation learning from high-dimensional biological data.
• Contribute to the design and implementation of workflows for integrative analysis of multiomic datasets (bulk, single-cell, spatial, and multimodal).
• Investigate, develop, and apply approaches for multimodal data fusion, cross-dataset integration, and transfer learning.
• Train, adapt and evaluate self-supervised and foundation models for omics data.
• Develop and apply interpretable models linking molecular states to disease trajectories and host-response phenotypes.
• Process and analyze large-scale sequencing and other omics datasets.
• Present research findings at seminars, conferences, and technical meetings.
• Contribute to research design and project execution.
• Collaborate in a multidisciplinary team environment.
• Publish results in peer-reviewed journals.

Skills

Machine Learning

Python

R

PyTorch

TensorFlow

Linux/Unix

Communication

Collaboration

Education

PhD in Computational Biology or related field

Tools

Python

R

PyTorch

TensorFlow

Job description

Lawrence Livermore National Laboratory seeks a Postdoctoral Research Staff Member to conduct research at the intersection of super intelligence, machine learning, and computational biology. You will integrate multiomic data to identify molecular programs linked to disease progression and host-response phenotypes.

Based in LLNL's Integrative Multi-Omics Group, you will work with senior scientists on high-dimensional data at scale, train state-of-the-art models, and publish in peer-reviewed