Postdoctoral Associate: Unsupervised Learning for DNA/RNA Molecular Dynamics

Overview Interpretable DNA/RNA Ensemble Quantification (molecular dynamics, machine learning, clustering, and measurement analysis). Biophysical and Biomedical Measurement Group (Microsystems and Nanotechnology Division), National Institute of Standards and Technology (NIST) – Gaithersburg, MD. The Biophysical and Biomedical Measurement Group at NIST (Gaithersburg, MD) is seeking a postdoctoral research associate to advance a theory/computation project focused on classifying DNA and RNA ensembles using secondary‑structure‑based distance metrics and clustering. A central goal is to build hierarchical, interpretable ensemble representations that connect simulation‑derived clusters to experimental measurements/observables and statistical‑physics interpretation (e.g., energetic barriers and kinetic pathways). Related paper: Clustering DNA and RNA molecular dynamics ensembles via secondary structure. Responsibilities Develop, test, and extend secondary‑structure representations for DNA/RNA derived from molecular dynamics (MD) trajectories or experiment. Implement and optimize secondary‑structure distance metrics based on base‑pair reorganization, with careful handling of topology assumptions (e.g., consistent knot topology within clusters; extensions/generalizations to knotted and/or pseudoknotted structures as necessary). Build scalable clustering and model‑selection workflows for large MD datasets (k‑means, hierarchical clustering, density‑based clustering) and evaluate robustness (stability analyses and other diagnostics). Analyze large nucleic‑acid MD datasets via trajectory coarse‑graining and secondary‑structure time series; connect clustering outputs to kinetics and free‑energy landscape interpretation, including energetic barriers relevant to hybridization disruption/reorganization. Develop well‑documented, reproducible research software (version control, testing, packaging, interfaces) and publish/present results. Collaborate with experimental and device‑focused teams to connect theory outputs to measurement needs. Required Qualifications Ph.D. in physics, chemistry, biophysics, computational biology, applied mathematics, computer science, or a closely related field. Demonstrated experience with biomolecular simulation and/or trajectory analysis (strong preference for nucleic acids: DNA/RNA). Experience with coarse‑grained nucleic‑acid models, e.g., oxDNA/oxRNA or closely related CG frameworks. Strong scientific programming (Python expected; NumPy/SciPy; data handling; plotting; performance optimization) and ability to write maintainable, version‑controlled code. Practical understanding of clustering/unsupervised learning and distance‑metric design, including how choices affect outcomes and validation/robustness. Strong communication skills (written and oral). Highly Desired Experience extracting secondary structure from 3D structures/MD (base‑pair detection, hydrogen‑bond criteria, contact maps; secondary‑structure time series). Experience with MD packages and analysis tools (LAMMPS/NAMD/GROMACS and related). High‑performance computing experience (batch systems; parallel processing; profiling/optimization). Background in statistical mechanics / polymer physics / stochastic processes; free‑energy or kinetic modeling of conformational ensembles. Experience analyzing experimental data from single‑molecule and ensemble techniques. Application Process Send a CV, a brief statement describing your technical fit for this specific project (highlight relevant methods/tools and your role in developing or applying them), and names/contact info for 2–3 references to View email address on click.appcast.io. Contact Dr. Michael Zwolak – View email address on click.appcast.io Citizenship U.S. citizenship is preferred; for some appointment mechanisms, eligibility depends on citizenship. NIST is an Equal Opportunity Employer. #J-18808-Ljbffr