Postdoctoral Fellow (PREP0004633)

Johns Hopkins University; Baltimore (MD); On-site; USD 70,000 - 85,000; Full time

Job summary

Johns Hopkins University is seeking a PREP Research Associate to contribute to the NIST FRAME program in Baltimore. The role focuses on developing generative models for soft matter systems, requiring a PhD in relevant fields like machine learning or materials science.

The ideal candidate will possess strong Python skills and a demonstrated ability to conduct independent research. Responsibilities include collaborating with experimental teams and presenting findings at meetings.

Qualifications

• PhD completed by the start date in a relevant field.
• Strong skills with Python and modern machine-learning stacks.
• Demonstrated ability to execute independent research and publish.

Responsibilities

• Develop, train, and validate generative models for soft matter systems.
• Design model architectures and training pipelines.
• Collaborate with experimentalists and present results.

Skills

Python programming

Generative modeling

Machine learning

Collaboration

Scientific data engineering

Education

PhD in machine learning, computer science, physics, chemistry, materials science, chemical engineering, or a related field

Tools

PyTorch

Job description

PREP Research Associate

Position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). The associate will perform technical work supporting the collaboration's scientific research.

Research Title

Bioformulation Digital Twin Developer

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

• 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.

Qualifications

• 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.

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.