90 developer-"https:" "https:" "https:" "https:" "Computer Vision Center" research jobs at NIST
Sort by
Refine Your Search
-
/) infrastructure uses a variety of methods such as density functional theory, graph neural networks, computer vision, classical force field, and natural language processing. We are currently focusing on
-
reviews. Advisers name email phone Vladimir Diky [email protected] 303.497.4124 Description The widespread use of aqueous chemical models, incorporated into computer codes, for both pure and
-
inspection and browsing capabilities. A small subset of project relevant publications is listed below. Requirements: A candidate should have at least a master’s degree in computer science or related
-
Develop extremely high sensitivity, multi-species measurement capability with cavity enhancement Explore quantum methods to improve sensitivity of DCS References: https://www.nist.gov/programs-projects
-
More information about the fire research group https://www.nist.gov/el/fire-research-division-73300/flammability-reduction-73304 The main projects in the group are: Exposure of firefighter's to per
-
goal of this project is to develop nano-electromagnetic imaging using scanning microwave, NV center, and magnetic resonance microscopy to characterize 2D materials, semiconductor devices, and biological
-
. References 1) "Fast and accurate prediction of material properties with three-body tight-binding model for the periodic table " https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials
-
fine structure), development of data-analysis approaches and computer software for simultaneous structural refinements using multiple types of data combined with ab initio theoretical modeling of
-
281 0908 Description Community Resilience Metrics The Community Resilience Program ( https://www.nist.gov/community-resilience) is developing science-based tools to assess resilience and support
-
, B. Heer, Additive manufacturing of multi-material structures, Mater. Sci. Eng. R Reports. 129 (2018) 1–16. https://doi.org/10.1016/j.mser.2018.04.001. [2] J. Guo, R. Floyd, S. Lowum, J.-P. Maria