SI/ML Computational Biologist - Postdoctoral Researcher

SI/ML Computational Biologist - Postdoctoral Researcher

• Full-time
• Organization: Physical and Life Sciences
• Category: Physical Life Sciences
• Employee Referral Bonus: Not applicable
• Job Code 1: PDS.1 Post-Dr Research Staff 1
• Pre-Employment Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)
• Pre-Placement Medical Exam: Not applicable
• Position Type: Post Doctoral
• Security Clearance: None/Position does not require US citizenship (assignments longer than 179 days require a federal background investigation)

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.

We are seeking a Postdoctoral Research Staff Member to conduct research at the intersection of super intelligence (SI), machine learning (ML), and computational biology. The successful candidate will apply and adapt state-of-the-art SI/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 a maximum of three years.

In this role you will

• 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.
• Perform other duties as assigned.

• Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA). See Additional Information section below for details.
• 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.

Desired Qualifications

• Experience with deep learning or probabilistic modeling approaches, such as variational autoencoders, scVI, or related methods.
• Experience with single-cell, spatial, and/or multimodal omics data.
• Experience with multiomic data integration, including multimodal single-cell datasets.
• Experience with transfer learning, domain adaptation, cross-dataset integration, or batch correction.
• Experience with transformers, self-supervised learning, or pretrained models for biological data.
• Experience training and scaling machine learning models on large datasets.
• Interest in immunology, host-pathogen biology, or disease modeling.

Pay: $123,048 Annually

#LI-Onsite

All your information will be kept confidential according to EEO guidelines.

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years. The postdoctoral appointment is a temporary, mentored training position intended for individuals in the early stages of their postdoctoral research careers. Consistent with the structure of the program, applicants must have received their Ph.D. (or equivalent doctorate) within five years of the anticipated start date.

Why Lawrence Livermore National Laboratory?

• Included in 2026Best Places to Work by Glassdoor!
• Flexible schedules (*depending on project needs)

None required.However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process. This process includes completing an online background investigation form and receiving approval of the background check.

National Defense Authorization Act (NDAA)

The 2025 National Defense Authorization Act (NDAA), Section 3112, generally prohibits citizens of China, Russia, Iran and North Korea without dual US citizenship or legal permanent residence from accessing specific non-public areas of national security or nuclear weapons facilities. The restrictions of NDAA Section 3112 apply to this position. To be qualified for this position, Candidates must be eligible to access the Laboratory in compliance with Section 3112.

Pre-Employment Drug Test

External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.

Wireless and Medical Devices

Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the useand/or possession ofmobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area whereyou are not permitted to have a personal and/or laboratory mobile devicein your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.

Ifyou useamedical device, whichpairs with a mobile device,you must still follow the rules concerningthe mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities requireseparate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.

Reasonable Accommodation

Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request.

CaliforniaPrivacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitlesjob applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here .