Postdoctoral Fellow; PREP

PREP Research Associate

This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest, thus requires that such institutions must be the recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas.

Employees in this position will perform technical work that underpins the scientific research of the collaboration.

Interpretable DNA/RNA Ensemble Quantification (Molecular dynamics, machine learning, measurement analysis)

This position will focus on theory and computation to classify DNA and RNA conformational 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). Work includes developing and validating analysis algorithms, implementing reproducible research software, and collaborating with experimental and device-focused teams to connect theory outputs to measurement needs.

• Develop, test, and extend ensemble representations for DNA/RNA and relate these to experimental observables
• Implement and optimize secondary-structure distance metrics based on base-pair reorganization
• Build scalable clustering and model-selection for large molecular dynamics datasets
• Present results at internal and external meetings and conferences
• Develop well-documented, reproducible research software and publish results

• U.S. Citizen Preferred
• A 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 frameworks
• Practical understanding of clustering/unsupervised learning and distance-metric design
• Strong scientific programming (Python preferred; Julia a plus) and ability to write maintainable, version-controlled code
• Background in statistics/statistical physics; ability to interpret ensembles in terms of kinetics and free-energy landscapes
• Strong written and oral communication skills and ability to collaborate in a multidisciplinary team; experience analyzing experimental data from single-molecule and ensemble techniques is a plus
• Vaccine requirements: at least one dose of the COVID-19 vaccine is strongly encouraged but no longer required (except for School of Medicine, which requires full vaccination). All faculty, staff, and students must receive the seasonal flu vaccine

The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University’s good faith belief at the time of posting. The actual compensation offered may vary based on geographic location, skills, work experience, internal equity, market conditions, education, training, and other factors.

Johns Hopkins offers a total rewards package that supports employees’ health, life, career, and retirement. More information can be found at (Use the "Apply for this Job" box below)..

The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. The university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or other legally protected characteristics. The university is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors or demographic characteristics that are irrelevant to the program involved.