Heavy-Ion Physics and Scientific Machine Learning - Postdoctoral Researcher

Lawrence Livermore National Laboratory; Livermore (CA); Hybrid; USD 105,000 - 142,000; Full time

Benefits offered by this job

Relocation Assistance

Education Reimbursement Program

Flexible schedules

401(k)

Job summary

Lawrence Livermore National Laboratory invites applications for a Postdoctoral Researcher in the Particle Physics Group. You will lead analyses of ATLAS and sPHENIX data, develop software and apply AI methods for nuclear physics research, and contribute to multimodal learning initiatives.

The role may offer hybrid work arrangements with potential home days. Excellent communication and collaboration across LLNL and external teams are essential.

Qualifications

• Ph.D. in Physics, Nuclear Physics, High Energy Physics or a closely related discipline.
• Demonstrated research experience in experimental nuclear or particle physics.
• Experience analyzing large scientific datasets using ROOT, Python, C++, or similar frameworks.
• Experience developing scientific software in Linux environments using modern programming practices.
• Experience with statistical analysis and uncertainty quantification.
• Excellent written and verbal communication skills and publications.
• Ability to work effectively in large international collaborations.

Responsibilities

• Perform physics analyses using ATLAS and sPHENIX data to study quark-gluon plasma and heavy-ion collisions.
• Participate in detector operations, calibration, performance studies, and data quality monitoring.
• Develop reconstruction, simulation, and analysis software using C++, Python, ROOT, and HPC resources.
• Apply state-of-the-art AI and scientific ML techniques for particle identification and jet reconstruction.
• Contribute to milestones for multimodal learning and detector performance improvements.
• Present research at collaboration meetings, DOE reviews, and conferences.
• Publish results in peer-reviewed journals and collaborate across LLNL and external institutions.
• Perform other duties as assigned.

Skills

ROOT

Python

C++

Linux

HPC/Grid

Statistics

Education

Ph.D. in Physics or related field

Tools

ROOT

Python

C++

Linux

HPC/Grid

Job description

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 Postdoctoral Researcher to contribute to experimental heavy-ion physics, detector development, and scientific machine learning within Lawrence Livermore National Laboratory's Particle Physics Group. You will play a leading role in the analysis of data from the sPHENIX experiment at Brookhaven National Laboratory and the ATLAS experiment at CERN, while contributing to DOE Office of Science Nuclear Physics (DOE-NP) and Genesis Mission-funded research milestones focused on advanced AI methods for nuclear physics. Research activities span precision measurements of jet quenching, ultra-peripheral collisions, detector performance studies, and the development of next-generation machine learning techniques for particle reconstruction and multimodal data analysis. This position is in the Particle Physics Group within the Nuclear and Chemical Sciences Division.

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.

You will

• Perform physics analyses using data from the ATLAS and sPHENIX experiments to study the quark-gluon plasma, jet quenching, and heavy-ion collisions.
• Participate in detector operations, commissioning, calibration, performance studies, and data quality monitoring within the ATLAS and sPHENIX collaborations.
• Develop reconstruction, simulation, and analysis software using modern C++, Python, ROOT, and HPC/Grid computing resources for processing multi-petabyte experimental datasets.
• Develop and apply state-of-the-art artificial intelligence and scientific machine learning techniques for particle identification, jet reconstruction, and event interpretation.
• Contribute to DOE-NP and Genesis-funded project milestones focused on multimodal learning, detector performance improvements, and scalable AI methods for experimental nuclear physics.
• Present research at collaboration meetings, DOE reviews, workshops, and international conferences.
• Publish results in leading peer-reviewed journals.
• Collaborate with physicists, computer scientists, and applied mathematicians across LLNL and external institutions.
• Perform other duties as assigned.

Qualifications

• Ph.D. in Physics, Nuclear Physics, High Energy Physics or a closely related discipline.
• Demonstrated research experience in experimental nuclear or particle physics.
• Experience analyzing large scientific datasets using ROOT, Python, C++, or similar scientific software frameworks.
• Experience developing scientific software in Linux environments using modern programming practices.
• Experience with statistical analysis and uncertainty quantification.
• Excellent written and verbal communication skills, including publications and scientific presentations.
• Ability to work effectively in large international collaborations.

Qualifications We Desire

• Experience with the ATLAS, sPHENIX, RHIC, LHC, CMS, ALICE, or STAR collider experiments.
• Experience with jet physics, heavy-ion collisions, and detector performance studies.
• Experience in scientific machine learning, deep learning, foundation models, or multimodal AI.
• Experience with GPU programming, high-performance computing, distributed computing, or large-scale workflow management.
• Experience developing reconstruction, simulation, or detector calibration software.
• Familiarity with modern machine learning frameworks such as PyTorch and TensorFlow.

Pay Range

$123,048 Annually

This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. 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, 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!
• FlexibleBenefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
• Our values - visithttps://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 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.

Equal Employment Opportunity

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 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.