Postdoctoral Appointee - Molecular Dynamics Theory, Onsite

What Your Job Will Be Like

We seek a motivated postdoctoral researcher with interest in the interface between machine learning, development of molecular dynamics theory, and applications to chemical problems. The successful candidate will develop and apply advanced quantum and classical molecular dynamics methods to investigate chemically relevant systems and predict experimentally observable phenomena. This position involves integrating modern data-driven and machine learning approaches with electronic structure theory, spectroscopy, and molecular simulation workflows to address challenging problems in chemical physics and materials science.

The candidate will work within a collaborative multi-investigator research environment while contributing to method development, high-performance scientific computing, and dissemination of results through publications and scientific presentations.

• Utilize both external and internal software packages to compute molecular dynamics trajectories.
• Develop new theoretical methods in quantum and classical molecular dynamics.
• Integrate modern machine learning methodologies with computational and experimental analysis methods.
• Predict observables computationally and compare with experimental data.
• Grasp the larger goals of each project, proposing innovative solutions and new directions.

You will be expected to partner with a multi-investigator team, present results at national/international meetings, and publish research in high profile peer-reviewed journals.

Due to the nature of the work, the selected applicant must be able to work onsite.

• PhD degree in chemistry, physics, or a closely related field
• Significant experience with theoretical methods development in electronic structure or molecular dynamics
• Significant experience with applications of electronic structure and molecular dynamics methods to chemical problems

• Demonstrated expertise in algorithm development and computer programming
• Experience with modern machine learning tools for chemical systems
• Ability to address complex problems creatively, and work effectively both independently and in collaboration with other researchers.
• Strong record of publications in peer-reviewed journals and presentations at scientific conferences
• Experience with Q-Chem, MOLPRO, CFOUR, Psi4, or similar software packages.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status and any other protected class under state or federal law.