Job description

Requisition Id 17231 Overview: 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). Major Duties/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. Basic Qualifications: • 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 . Preferred Qualifications: • 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. Special Requirements: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding. • For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation. • To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws. • For foreign national candidates: If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment. Please submit three letters of reference when applying to this position. You may upload these directly to your application or have them sent to Postdocrecruitment@ornl.gov with the position title and number referenced in the subject line. Instructions to upload documents to your candidate profile: • Login to your account via jobs.ornl.gov • View Profile • Under the My Documents section, select Add a Document About ORNL: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation. ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience. Other benefits include the following: 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, and Employee Discounts. This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired. We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment. If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov. ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.

Requirements

Must-haves

Requisition Id 17231
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.
A Ph.D. in Chemistry, Physics, Materials Science, Chemical Engineering, Computer Science, Applied Mathematics, or a related discipline , completed within the last five years.

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.

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Keywords to mirror

machine learningpython

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Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave

Wellness