Postdoctoral Research Associate - Atomistic Machine Learning

Oak Ridge National Laboratory • Oak Ridge, TN

28d • Onsite

About The Position

We are seeking a Postdoctoral Research Associate who will focus on the development, application, and optimization of an active learning workflow for dataset generation of atomic structures and training of machine learning interatomic potentials (MLIPs) in a way that maximizes the capability of high-performance computing (HPC) platforms available in the Oak Ridge Leadership Computing Facility (OLCF). This position resides in the Advanced Computing for Chemistry and Materials Group in the National Center for Computational Sciences (NCCS), Computing and Computational Sciences Directorate (CCSD), at Oak Ridge National Laboratory (ORNL).

Requirements

• A Ph.D. in Chemistry, Physics, Materials Science, Chemical Engineering, Computer Science, Applied Mathematics, or a related discipline, completed within the last five years.
• Experience with molecular dynamics (MD) simulations.
• Experience with machine learning interatomic potentials (MLIPs) or atomistic machine learning methods applied to chemistry, physics, or materials science.
• Experience with Python and scientific software development.
• Knowledge of fundamental machine learning concepts, including the training, validation, and application of neural network models.
• Experience with Linux-based scientific computing environments for HPC.

Nice To Haves

• Significant experience developing, training, fine-tuning, or applying MLIPs, including graph neural network-based models, equivariant models, or pretrained universal potentials.
• Experience with MLIP frameworks such as HIP-NN, MACE, NequIP, Allegro, ANI, and UMA.
• Experience with atomistic simulation software such as LAMMPS, ASE, GROMACS, OpenMM, TorchSim, or related MD simulation packages.
• Experience with MLIP-based molecular dynamics, including system preparation, simulation execution, trajectory analysis, and evaluation of model stability and accuracy.
• Experience with active learning, uncertainty quantification, configuration selection, automated dataset generation, or related methodologies.
• Experience developing and managing automated and parallel scientific workflows, including task execution systems, dependency management, and pipeline execution on distributed computing resources.
• Experience with HPC systems, including running and managing applications on multi-node, GPU-accelerated systems.
• Experience with HPC resource and workload management systems such as Slurm, using workflow methodologies and/or tools such as Parallel, Flux or Parsl.
• Familiarity with concepts of parallelism, scalability, and efficient utilization of computational resources.
• Experience with GPU accelerators and computing ecosystems such as CUDA and/or AMD ROCm/HIP.
• Experience with performance profiling and optimization of scientific applications, particularly molecular dynamics and machine learning applications.
• Familiarity with electronic structure and quantum chemistry methods, such as density functional theory (DFT), and their use in generating reference data for MLIPs.
• Experience with modern scientific software engineering practices, including Git, automated testing, software environments, continuous integration, documentation, and collaborative development.
• Familiarity with parallelization and high-performance programming techniques such as MPI, OpenMP, multiprocessing, or distributed computing in Python.
• Track record of publishing research results in peer-reviewed scientific journals and presenting research at scientific conferences.
• Excellent written and oral communication skills.
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory.
• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs.

Responsibilities

• Develop, implement, and apply machine learning interatomic potentials (MLIPs) for molecular dynamics simulations.
• Develop and optimize automated workflows for data generation and active learning, including molecular dynamics (MD) sampling, configuration selection, labeling with reference methods, and model training.
• Implement and optimize large-scale MD simulations using MLIPs and other atomistic models on HPC resources.
• Evaluate, integrate, and optimize different MLIP architectures and implementations, including pretrained universal models and models tailored to specific chemical and materials systems.
• Develop, debug, profile, and optimize scientific codes and workflows on massively parallel computing systems, including GPU-accelerated platforms.
• Develop and maintain scientific software, primarily in Python, using modern practices for software development, testing, documentation, and version control.
• Work with tools and libraries for atomistic simulation and machine learning, such as LAMMPS, ASE, PyTorch, and related tools.
• Develop and evaluate strategies to improve the performance, scalability, efficiency, and robustness of MD simulations, MLIP inference, data generation, and model training.
• Collaborate with computational scientists, HPC specialists, software developers, and researchers across scientific domains to develop and apply new computational methods.
• Conduct collaborative research, analyze and communicate scientific results, and contribute to peer-reviewed journal articles, conference papers and presentations, and technical documentation.
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success.

Benefits

• medical and retirement plans
• flexible work hours
• on-site fitness
• banking
• cafeteria facilities
• Prescription Drug Plan
• Dental Plan
• Vision Plan
• 401(k) Retirement Plan
• Contributory Pension Plan
• Life Insurance
• Disability Benefits
• Generous Vacation and Holidays
• Parental Leave
• Legal Insurance with Identity Theft Protection
• Employee Assistance Plan
• Flexible Spending Accounts
• Health Savings Accounts
• Wellness Programs
• Educational Assistance
• Relocation Assistance
• Employee Discounts

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree