It has long been a dream to design molecular devices and machines in Nature’s image—systems that integrate information processing, sensing, and actuation at the nanoscale. DNA and RNA are especially powerful building blocks, as the sequence encodes structure through base pairing. Yet real systems rarely behave “ideally”: thermodynamics, kinetics, topology, and environmental fluctuations shape microscopic trajectories and determine yield, robustness, and function.

Our research develops theory and computation for biomolecular folding and assembly, design, and measurement—bridging molecular mechanisms to what experiments can observe. We combine statistical physics with simulation, AI/ML, and data-driven algorithms to tackle problems spanning multiple length and time scales. Projects are carried out in a highly collaborative environment within NIST’s Microsystems and Nanotechnology Division, with close connections to experimental teams working on nanopores, spectroscopy, sensors, microfluidics, and microphysiological systems.

Example research directions (not exhaustive)

• DNA/RNA structural-ensemble classification: develop interpretable ensemble metrics (e.g., secondary-structure distance measures) and clustering techniques for large molecular-dynamics (MD) datasets; connect clusters to energetic barriers and experimentally relevant observables.
• Biomolecular self-assembly and design rules: quantify folding/assembly pathways, failure mechanisms, and uncertainty; extract thermodynamics/kinetics from quantitative fluorescence and melt/anneal experiments.
• Nanopore and nanofluidic biophysics: model ion/biomolecule transport, selectivity, access resistance, and translocation barriers to guide measurement strategies and device design.
• Non-equilibrium processes and energy landscapes: connect topology/landscape structure to transport and relaxation in biomolecular and nanoscale systems.

Desired background/skills

We seek candidates with deep expertise in one or more of: theoretical/computational biophysics; statistical mechanics; molecular simulation (all-atom and/or coarse-grained); stochastic processes and kinetic modeling; scientific computing and reproducible software; and/or machine learning for physical/biophysical data. Strong programming skills (e.g., Python/C/C++), experience with large datasets/HPC, and a drive to connect models to measurement are highly valued.

How to apply/contact

Prospective applicants are encouraged to email a short note describing technical fit and interests, along with a CV, to: mpz@nist.gov

Group overview: Biophysical and Biomedical Measurement Group

References (selected)

• Clustering DNA and RNA molecular dynamics ensembles via secondary structure, S. Baral & M. Zwolak, Biophysical Journal 125, 515 (2026)
• Single–Molecule Biodosimetry, M. Lamontagne, S. M. Newell, I. M. Pazos, R. E. Tosh, J. C. Polf, M. Zwolak, & J. W. F. Robertson, Analytical Chemistry 97, 22004 (2025)
• Best practice for improved accuracy: A critical reassessment of van't Hoff analysis of melt curves, J. M. Majikes, M. Zwolak, & J. A. Liddle, Biophysical Journal 121, 1986 (2022)
• Failure Mechanisms in DNA Self-Assembly: Barriers to Single-Fold Yield, J. M. Majikes, P. N. Patrone, A. J. Kearsley, M. Zwolak, & J. A. Liddle, ACS Nano 15, 3284 (2021)
• Revealing Thermodynamics of DNA Origami Folding via Affine Transformations and Quantitative Fluorescence Reporting, J. M. Majikes, P. N. Patrone, D. Schiffels, M. Zwolak, A. J. Kearsley, S. P. Forry, & J. A. Liddle, Nucleic Acids Research 48, 5268 (2020)
• Topology, Landscapes, and Biomolecular Energy Transport, J. E. Elenewski, K. A. Velizhanin, & M. Zwolak, Nature Communications 10, 4662 (2019)
• Optimal transport and colossal ionic mechano-conductance in graphene crown ethers, S. Sahu, J. E. Elenewski, C. Rohmann, & M. Zwolak, Science Advances 5, eaaw5478 (2019)
• Colloquium: Ionic phenomena in porous 2D materials and their applications, S. Sahu & M. Zwolak, Reviews of Modern Physics 91, 021004 (2019)
• The golden aspect ratio for ion transport, S. Sahu & M. Zwolak, Physical Review E 98, 012404 (2018)
• Maxwell-Hall access resistance in graphene nanopores, S. Sahu & M. Zwolak, Physical Chemistry Chemical Physics 20, 4646 (2018)
• Ionic selectivity and filtration from fragmented dehydration in multilayer graphene nanopores, S. Sahu & M. Zwolak, Nanoscale 9, 11424 (2017)
• Dehydration as a Universal Mechanism for Ion Selectivity in Graphene and Other Atomically Thin Pores., S. Sahu, M. Di Ventra, & M. Zwolak, Nano Letters 17, 4719 (2017)

DNA; RNA; nucleic-acid folding; secondary structure; base pairing; molecular dynamics; conformational ensembles; interpretable representations; clustering; unsupervised learning; machine learning; distance metrics; coarse-graining; statistical physics; thermodynamics; kinetics; free-energy landscapes; self-assembly; DNA origami; fluorescence melting/annealing; nanopores; nanofluidics; ion transport; selectivity; access resistance; translocation barriers; stochastic processes; kinetic modeling; high-performance computing; scientific computing; reproducible research

citizenship

Open to U.S. citizens

level

Open to Postdoctoral applicants