Computational Biologist (ML) Postdoctoral Researcher

SmartRecruiters, Inc.; Livermore (CA); Hybrid; USD 122,000 - 143,000; Full time

Job summary

Lawrence Livermore National Laboratory (LLNL) is seeking a Postdoctoral Researcher to advance computational structural biology, focusing on predicting protein-protein interactions in host-pathogen systems using deep learning.

You will work with DL-based methods, protein sequences, and structures, collaborating with computational and experimental biologists to push next-generation protein design tools and models forward.

Qualifications

• Experience developing and implementing deep learning models and algorithms.
• Experience with protein sequence and structure, bioinformatics, and structure modeling.
• Ability to work independently and in cross-disciplinary teams.
• Proven publication record or software releases demonstrating DL expertise.

Responsibilities

• Conduct research to design, analyze, and extend DL-based tools for protein interaction prediction.
• Develop sequence- and structure-computational frameworks and analysis tools.
• Collaborate with universities, industry, and national labs to advance simulation efforts.

Skills

Deep learning

Python

UNIX

HPC

Bioinformatics

Education

PhD in Life Sciences / Computational Biology

Tools

PyTorch

RoseTTAFold3

AlphaFold3

ESMFold2

Job description

Computational Biologist (ML) Postdoctoral Researcher

• Full-time
• 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 have an opening for a highly motivated Postdoctoral Researcher to conduct research in computational structural biology to develop methods for predicting protein-protein interactions in host-pathogen systems using deep learning (DL).

Current structure prediction tools such as RoseTTAFold3, AlphaFold3, and ESMFold2 excel at monomeric structure prediction and predicting known protein complexes. However, these tools struggle to identify which proteins interact and which do not. You will work to develop specific models that can discriminate between interacting and non-interacting proteins.

You will be an integral member of an interdisciplinary, cross-institution team working with computational biologists, and experimental biologists.You will leverage computational tools and work to develop new DL-based approaches and tools to predict binary interactions, specificity, and structure. You will also work closely with an existing team of computational biologists to understand current capabilities and jointly develop a vision for development of next generation protein design models and tools.You will present your work regularly and publish your research and findings, which includes occasional travel. This position is in the Computational Engineering Division (CED), within the Engineering Directorate.

Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week.

In this position, you will

• Conduct research, and contribute to designing, analyzing, and extending DL-based tools for prediction and optimization of protein interaction specificity.
• Participate in the development of protein sequence and structure computational frameworks and analysis tools.
• Collaborate with external partners (Universities, Industry, other National Laboratories) to advance computational biology simulation efforts.
• Prepare complex and detailed progress reports, written analyses, and verbal briefings to support project needs and deadlines and to present research results to sponsors.
• Independently pursue the development of new and innovative research methods relevant to the needs of Laboratory programs and/or external funding agencies.
• Contribute to proposals and statements of work.
• Publish research results in peer-reviewed scientific or technical journals and present results at external conferences, seminars, and/or technical meetings.
• Travel as needed to coordinate with research collaborators and to attend external meetings and conferences.
• Perform other duties as assigned.
• PhD in Life Sciences, Computational Biology, or Life-science applied ML, Statistics, Computer Science or Mathematics, or related technical or scientific field.
• Experience developing and implementing deep learning models and algorithms, extracting embeddings, or fine-tuning models using modern software libraries such as PyTorch, or similar as evidenced through publications or software releases.
• Experience working with protein sequence and structure; knowledge in bioinformatics and protein structure modeling sufficient to communicate effectively with team members.
• Ability to work independently on defined research projects, as well as a member of a team with a diverse set of scientists, engineers, and other technical and administrative staff.
• Programming experience with Python and expertise with UNIX and high-performance computing environments.
• Ability to develop independent research projects as demonstrated through publication of peer-reviewed manuscripts.
• Ability to travel as necessary.

Qualifications We Desire

• Understanding and experience in protein bioinformatics, protein structure prediction, and/or protein function prediction.
• Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflows.
• Experience in collaborating with experimental and computational biologists.

Pay Range

$122,028 - $143,328 Annually

This is the lowest to highest salary in good faith we would pay for this role at the time of this posting. Pay will not be below any applicable local minimum wage. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs.

#LI-Hybrid

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire 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 use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.

If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate 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.

We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

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 entitles job 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 .

By clicking the link above or any third-party link within this posting, you are leaving this site and going to a third-party website where the third-party website's terms and privacy policy apply