Heavy-Ion Physics and Scientific Machine Learning - Postdoctoral Researcher

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 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. 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. #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 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. 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