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 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.
Interested candidates should submit a complete application that includes:
- Cover letter (1-2pgs) describing the following:
- Research interests
- A description of graduate work and greatest scientific accomplishments
- How previous work (postgraduate/graduate/prior research experience) makes you a strong candidate for developing ML methods for predicting protein-protein interactions.
- What are your career goals (briefly)
- How do you seek to approach this research area? (~1 paragraph)
- Curriculum vitae including full publication list
- Contact information for three professional references
A complete application includes a cover letter, CV, and references. Incomplete applications will not be considered.
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.
Qualifications
- PhD in Life Sciences, Computational Biology, or Life-science applied ML, Statistics, Computer Science or Mathematics, or a 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.
Additional Information
#LI-Hybrid
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 2026 Best Places to Work by Glassdoor!
- Flexible Benefits Package
- 401(k)
- Relocation Assistance
- Education Reimbursement Program
- Flexible schedules (*depending on project needs)
- Our values - visit https://www.llnl.gov/inclusion/our-values
Security Clearance
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.
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Equal Employment Opportunity
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