Postdoctoral researcher
Project
PREP0004633
Overview
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.
Bioformulation Digital Twin Developer
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.
Research Proposal
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.
NIST Sponsor
Tyler Martin
Group
Polymers Processing Group
Salary / Hourly Rate {Min}
$85,000.00
Schedule of Appointment
Full time
Start Date
Sponsor email
Work Location
Onsite NIST (Gaithersburg, MD)
Salary / Hourly rate {Max}
$100,000.00
Total Hours per week
40
End Date