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Measurement and analysis of landfill emissions via high-accuracy observations of atmospheric greenhouse gas (CO2 and methane) concentration using both airborne and surface observational platforms.

Development of an autonomous platform for growing and testing novel metal organic framework (MOF) materials for applications in carbon capture

Research whether the distribution of the depth error across a single image at a certain distance is “the same” as the distribution of the depth error across a single image taken from a significantly different distance. Research the effects of various factors (part color, part surface properties, bin color, bin depth, ratio of part size to bin size, part distribution in a bin) on bin picking performance (e.g., cycle time, pose uncertainty, etc.).

Research Intern – Science and Technology

On-wafer complex permittivity measurements of piezoelectric materials

The researcher will first investigate optimization-based and machine learning-based methods for CD inference of nanofeature arrays. First, the researcher will investigate an optimization-based method for CD inference to improve the method, to assess its ability to converge to the global minimum, and to quantify the uncertainty of the method. Second, the researcher will use neural networks and machine learning-based algorithms to design an AI-based method to estimate CDs of nanofeatures in arrays based on reflectance spectra of the nanofeature arrays.

Work will include handling, preparation and preconcentration of gas-phase samples, performing ultrasensitive CRDS, and interpreting data to infer the origins and/or composition of the carbon source material. Success will require the design and assembly of a CO 2 preconcentration system, and the control and automation of laser-based instrumentation. Further work will include the use of metrology tools like optical frequency combs and NIST pressure and temperature standards to establish traceability.

Synthetic biology technician

A Platform for Uncertainty Quantification of Absolute Particle Count

The Sensor Science Division at the National Institute of Standards and Technology (NIST) is seeking for several highly motivated research scientists to aid the multi-year development of next-generation integrated photonics-based quantum-SI temperature sensors and primary standards on-a-chip. At NIST, you will gain research experience and measurement skills that are highly valuable for a career in academia, industry, or a national laboratory.