Sort by
Refine Your Search
-
NIST only participates in the February and August reviews. This program explores the development and evaluation of integrated measurement strategies for biopharmaceutical manufacturing using the NIST Biomanufacturing Testbed and the NISTCHO/cNISTmAb model system. Projects are focused on...
-
RAP opportunity at National Institute of Standards and Technology NIST Design, Characterization, and Modeling of Sequence Controlled Polymers Location Material Measurement Laboratory, Materials
-
Electrochemical Surface Science of Metal-Electrolyte Interfaces: Theory and Experiment NIST only participates in the February and August reviews. Accurate models of the electrochemical interface
-
RAP opportunity at National Institute of Standards and Technology NIST Integrating Data and Computational Tools for Advanced Materials Design Location Material Measurement Laboratory, Materials
-
RAP opportunity at National Institute of Standards and Technology NIST Combining Theory, Simulation, Machine Learning, and Autonomous Experiments for Industrial Formulation Discovery Location
-
RAP opportunity at National Institute of Standards and Technology NIST Model Polymer Gels and Networks for Rational Sustainable Design Location Material Measurement Laboratory, Materials Science
-
RAP opportunity at National Institute of Standards and Technology NIST Scientific machine learning methods for trustable accelerated materials characterization and design Location Material
-
into therapeutic applications is design principles for predictably and reproducibly culturing cells and efficiently differentiating them into cell types of interest. Typical culture conditions today result in
-
RAP opportunity at National Institute of Standards and Technology NIST Adsorption Sciences: Metrology and Innovation Location Material Measurement Laboratory, Chemical Sciences Division
-
-driven materials design" https://www.nature.com/articles/s41524-020-00440-1 2. https://jarvis.nist.gov/ 3. https://www.nist.gov/people/kamal-choudhary Machine learning; Density functional theory; force