Sort by
Refine Your Search
-
NIST only participates in the February and August reviews. NIST has recently launched a program to develop high accuracy 3D thermal imaging and control using thermosensitive magnetic nano-objects
-
RAP opportunity at National Institute of Standards and Technology NIST Indoor Localization and Tracking Location Information Technology Laboratory, Advanced Network Technologies Division
-
Advancing the state of the art in measurements of sound, vibration, force, acceleration and velocity
; Machine Learning; Artificial Intelligence; AI; PINN; Sensor Networks; Sensor Fusion; Optomechanics; Interferometry; Frequency Comb; Photonics; Acoustics; Sound; Sensing; Optics; Bayesian; Statistics; Signal
-
learning with machine-controlled measurement tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods
-
guiding materials measurement experiments to acclerate learning the synthesis-process-structure-property relationship. Machine learning methods include, but are not limited to, Bayesian inference
-
characterization tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods. A key challenge is the integration
-
nuclear nonproliferation. User communities have expressed a need for test objects or “phantoms” with radioactive features of precisely defined spatial dimensions and traceable activity that can be used
-
nanocomposites containing these networks. Our objective is to develop metrologies to understand how morphology and functionalization affect the alternating current (AC) conductivity of composite materials
-
are developing object-oriented computational tools for the analysis of materials with complex microstructures. Starting from a digitized micrograph, the program identifies features in the image, assigns material
-
. Opportunities exist for (1) developing a framework for design of buildings and infrastructure systems to meet recovery-based objectives (functional recovery framework), (2) developing design criteria