Postdoctoral Fellow (PREP0004633) - #Faculty

PREP Research Associate This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. The PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields. Individuals in this position will perform technical work supporting the collaboration's scientific research. Research Title: Bioformulation Digital Twin Developer The work will entail: The work will support the NIST FRAME (Foundational Representation and Assimilation for Multimodal Experiments) program, which is developing a modeling and simulation ecosystem centered on a coherent material digital twin. The associate will develop and validate generative AI and physics-grounded modeling approaches that reconcile multimodal measurements, including SAXS, SANS, RSoXS, Cryo-EM, light scattering, and related observables. Initial demonstrations will focus on bioformulations and related soft nanocarrier platforms, with an emphasis on reproducible computational workflows, uncertainty quantification, and close collaboration with experimental and instrument teams. U.S. Citizen Preferred Key responsibilities will include but are not limited to: Develop, train, and validate generative models for 3D structure and mesostructure of soft matter systems, with an initial emphasis on bioformulations and related nanocarrier platforms. Design model architectures and training pipelines, including VAE and latent-variable models, diffusion and score-based models, autoregressive models, normalizing flows, or related approaches. Create representations that bridge cartoon or parametric structure generators, material digital twin representations, and experimental signatures such as SAXS, SANS, RSoXS, Cryo-EM, and light scattering. Incorporate uncertainty quantification, calibration, and validation workflows so that model outputs can be compared rigorously with experimental observables. Define metrics and benchmarks for physical plausibility, diversity, reproducibility, and fidelity to measured data. Collaborate with experimentalists and instrument teams to close the loop between formulation, structure, measurement, analysis, and model update. Present results at internal meetings and occasional meetings with external stakeholders, including collaborators in measurement science, materials modeling, and user-facility instrumentation. Produce open and reproducible research outputs, including documented code, datasets and metadata, model cards or equivalent documentation, protocols, and publications. Ensure that results, protocols, software datasets, metadata, and documentation are archived or otherwise transmitted to the larger organization. PhD completed by the start date in machine learning, computer science, physics, chemistry, materials science, chemical engineering, or a related field. Strong Python programming skills and experience with a modern machine-learning stack, including PyTorch and GPU or HPC workflows. Demonstrated ability to execute independent research, communicate results, and publish in peer-reviewed venues. Demonstrated experience in generative modeling for scientific data is strongly preferred. Experience with generative AI for scientific or physical systems, including 3D fields, images or volumes, point clouds, graphs, or related structured representations, is highly desired. Experience with soft matter, self-assembly, colloids, surfactants, polymers, biomaterials, or bioformulations is highly desired. Experience with inverse problems or simulation-to-measurement workflows, including learned forward models, differentiable physics, or amortized inference, is highly desired. Experience with scientific data engineering, including dataset versioning, provenance, metadata, or reproducible research workflows, is highly desired. Strong oral and written communication skills and ability to work collaboratively with experimentalists, instrument scientists, and computational researchers. Salary: $85,000 - $100,000 Privacy Act Statement Authority: 15 U.S.C. 278g-1(e)(1) and (e)(3) and 15 U.S.C. 272(b) and (c) Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) ( which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty. PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate administrative functions of the PREP Program. Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates. Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated. Salary Range 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. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University. Total Rewards Johns Hopkins offers a total rewards package that supports our employees' health, life, career and retirement. More information can be found here: Equal Opportunity Employer The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To that end, 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. Pre-Employment Information If you are interested in applying for employment with Johns Hopkins University and require special assistance or accommodation during any part of the pre-employment process, please contact the HR Business Services Office at View email address on click.appcast.io. For TTY users, call via Maryland Relay or dial 711. For more information about workplace accommodations at Johns Hopkins University for disabilities, medical conditions (including medical conditions related to pregnancy or childbirth), accessibility, or religious reasons, please visit accessibility.jhu.edu. Background Checks After receiving a conditional offer, the successful candidate(s) for this position will be subject to a pre-employment background check including education verification. When deciding whether a candidate's conviction history is job-disqualifying, the University considers the nature and gravity of the offense, the time that has passed since the conviction, and the nature of the job being sought. EEO is the Law Vaccine Requirements Johns Hopkins University strongly encourages, but no longer requires, at least one dose of the COVID-19 vaccine. This change does not apply to the School of Medicine (SOM). SOM hires must be fully vaccinated with an FDA COVID-19 vaccination and provide proof of vaccination status. We still require all faculty, staff, and students to receive the seasonal flu vaccine. Exceptions to the seasonal flu vaccine or COVID-19 vaccine (for SOM) requirement(s) may be provided to individuals with sincerely held religious beliefs or medical conditions that preclude them from receiving the vaccine. Requests for an exception must be submitted to the JHU vaccination registry. For additional information, applicants for SOM positions should visit and all other JHU applicants should visit The following additional vaccine requirements may apply, depending upon your campus. Please contact the hiring department for more information. The pre-employment physical for positions in clinical areas, laboratories, working with research subjects, or involving community contact requires documentation of immune status against Rubella (German measles), Rubeola (Measles), Mumps, Varicella (chickenpox), Hepatitis B and documentation of having received the Tdap (Tetanus, diphtheria, pertussis) vaccination. This may include documentation of having two (2) MMR vaccines; two (2) Varicella vaccines; or antibody status to these diseases from laboratory testing. Blood tests for immunities to these diseases are ordinarily included in the pre-employment physical exam except for those candidates who provide results of blood tests or immunization documentation from their own health care providers. Any vaccinations required for these diseases will be given at no cost in our Occupational Health office. Job Type: Full Time #J-18808-Ljbffr Johns Hopkins University