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Oak Ridge National Laboratory
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Postdoctoral Research Associate in Computational Chemistry, Machine Learning Potentials, and Free Energy Methods Oak Ridge National Laboratory in United States
Degree Level
Postdoc
Field of study
Chemistry
Funding
The position is an employee postdoctoral appointment offering competitive pay and benefits. Benefits include medical, dental, vision, retirement plans, life and disability insurance, paid vacation and holidays, parental leave, flexible spending and health savings accounts, wellness programs, educational assistance, relocation assistance, and other employee benefits. The appointment is up to 24 months, with possible extension subject to performance and funding availability.
Country
United States
University
Oak Ridge National Laboratory
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About this position
Oak Ridge National Laboratory is seeking a Postdoctoral Research Associate in Computational Chemistry for the Chemical Separations Group within the Separations and Polymer Section, Chemical Sciences Division, Physical Sciences Directorate.
The research focuses on developing and applying machine learning interatomic potentials (MLIPs) and free-energy methods to ion separations, interfacial science, and energy-related processes. The postdoctoral researcher will develop, fine-tune, validate, and deploy MLIPs—including MACE and related architectures—for ions and ligands in single- and two-phase separation systems.
The project combines ab initio calculations, molecular dynamics, and advanced statistical mechanics to investigate ion complexation in bulk liquids and ion transfer across liquid–liquid interfaces. Using tools such as LAMMPS, OpenMM, and related simulation software, the researcher will study ion solvation, ligand binding, complexation free energies, ion transport, proton transfer, and the effects of interfacial structure and solvent organization on reaction and transfer free energies.
The researcher will work in a multidisciplinary, multi-institutional environment with specialists in theory, spectroscopy, organic synthesis, and separations. Collaboration with experimentalists will support interpretation of experimental results and help guide the design of new chelating agents. The role also involves presenting findings, publishing in peer-reviewed journals, and contributing to ORNL’s mission in energy, environmental, and security research.
Funding details
The position is an employee postdoctoral appointment offering competitive pay and benefits. Benefits include medical, dental, vision, retirement plans, life and disability insurance, paid vacation and holidays, parental leave, flexible spending and health savings accounts, wellness programs, educational assistance, relocation assistance, and other employee benefits. The appointment is up to 24 months, with possible extension subject to performance and funding availability.
What's required
Applicants must hold a PhD in theoretical or computational chemistry or a closely related field in physical chemistry or chemical physics, completed within five years before the application date, and must complete all degree requirements before starting. Required experience includes density functional theory and/or molecular dynamics using ab initio and machine-learning potentials, plus advanced statistical mechanics methods such as thermodynamic integration. Preferred qualifications include experience with MACE or related equivariant MLIPs, foundation models, transfer learning, fine-tuning or active learning; molecular simulations of ions in liquids; free-energy methods; electronic-structure calculations on high-performance computing systems; and excited-state methods. Applicants should demonstrate productive research and publication experience, strong written and oral communication, independence, creativity, teamwork, and the ability to work in a fast-paced environment. Three reference letters are required. Employment requires appropriate identification, background screening, and eligibility for an ORNL PIV credential or applicable LSSO determination for foreign nationals.
How to apply
Apply through the ORNL jobs portal using the Apply now option for requisition 17430. Upload your application documents to the candidate profile under My Documents, and submit three reference letters either through the application or by email to [email protected] with the position title and requisition number in the subject line.
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