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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.

Post-doctoral appointment to theoretically investigate novel materials for neuromorphic computing.

We are seeking a PREP post-doctoral candidate to fill an associate position within the Materials
Measurement Science Division. This postdoc will be a key member of a new project to develop an
autonomous platform for growing and testing novel metal-organic framework (MOF) materials for
applications in carbon capture (https://doi.org/10.1016/j.xcrp.2022.101063). Specifically, the position
will involve commissioning and operating a robotic platform for the synthesis of MOF materials along
with carrying out high-throughput characterization assays and providing chemical insight into the design
of an autonomous workflow.  Successful candidates must have a background in MOF (or other similar
chemical frameworks) synthesis and characterization.
 
The postdoc will join a group that is focused on pioneering applications of modern machine learning
methods, FAIR data infrastructure, and experimental automation for materials characterization
(metrology) methods across all portions of the structure-processing-properties-performance
relationship. Specific group research focus areas include the development of interpretable and
trustworthy algorithms for Scientific Artificial Intelligence and active learning, integrating FAIR data
management practices throughout the research lifecycle, and developing innovative on-demand
material synthesis and characterization platforms for closed-loop materials development.

The Biosystems and Biomaterials Division at NIST is seeking to hire a laboratory automation scientist or
engineer. The person hired for this position will help to lead the development of automation-based
methods to create a measurement infrastructure for engineering biology. They will work with a
multidisciplinary team to develop and program new automation methods and to facilitate rapid and
flexible adoption of automation systems for a variety of biological measurements.

This position will focus on the design, synthesis, and characterization of model thermoplastics, with
systematic variation of polymer sequence, chemistry, and architectures to generate libraries of
quantitative structure-property-performance relationships (validated by multiple measurement
methods. In addition, the project aims to develop methodologies to quantitatively identify and
characterize molar mass, branching content, and chemical composition of degraded of plastics from
recovered ocean debris and real-world and laboratory experiments.

There is growing evidence that firefighters are being exposed to per- and poly-fluoroalkyl
substances (PFAS) associated with their gear.  The source of the PFAS may be from the
firefighter gear (FFG) construction materials or manufacturing process, deterioration of the FFG
during service, and/or deposition on the FFG while on duty. To characterize and reduce the
firefighter’s risk of being exposed to PFAS, NIST is starting a new study to understand the type,
prevalence, and concentration of PFAS on the FFG, the source of PFAS, and the mechanism for
PFAS release from the FFG. This study is being conducted in collaboration with staff in MML.
This study has three main thrusts. 
 Thrust 1: Characterize PFAS associated with FFG textiles (e.g.; jacket, pants, gloves)
new/unused, and after stress.  Stressing is intended to mimic typical wear-and-tear
conditions while a Firefighter is on duty (e.g., elevated temperatures, abrasion, and
laundering). 
 Thrust 2: Characterize PFAS found at firefighter’s common workplace environments
(e.g., fire scene, fire truck, and fire station). Understanding not only what PFAS species
are identified but the source of the PFAS is essential to developing strategies to reducing
Firefighter’s risk of being exposed to PFAS. 
 Thrust 3: Coordinate and collaborate our research with the existing PFAS research
community who are studying PFAS from AFFF, human health effects from PFAS
exposure, etc..  The purpose of this is to use our data to help in the development of
a comprehensive strategy to reduce/eliminate Firefighter exposure to PFAS.

The student will help prepare and characterize novel metal-functionalized ion exchange resins for use in
direct air capture of carbon dioxide. These materials may include various ion exchange resins, and
various metal ions. A variety of different methodologies will be used to prepare these materials, such as
flowing through a column. Analytical methods such as molecular spectroscopy, thermogravimetric
analysis, sorption isotherms, and long-term cycling tests will be used to characterize the resultant
materials and their utility in direct air capture arrays. The student may propose improvements and
efficiencies to the procedures used to prepare the materials, analyze the data from the experiments,
develop summaries of analysis, and contribute to an expected scientific publication from this work.

The position is in the Applied Economics Office (AEO), a part of the Engineering Laboratory (EL) at NIST, which provides economic products and services through research and consulting to industry and government agencies in support of productivity enhancement, economic growth, and international competitiveness, with a focus on improving the life-cycle quality and economy of constructed facilities and manufacturing processes that support social and economic functions. AEO is integrated within EL’s major research thrusts: sustainability, energy conservation, community resilience planning, manufacturing, fire, smart grid, building construction, and safety. AEO delivers high-quality research and tool development that informs and assists stakeholders in their decision-making processes. The position will collaborate directly with the software development team in EL’s ELDST (Engineering Laboratory Data, Security, & Technology) that oversees AEO’s software development projects.

Experimental Quantum Network Science at NIST

With an eye on the emerging quantum internet, our efforts push measurement science at the single
photon level, and we are seeking a Ph.D. with experience in Quantum Information Science who is
interested in working with experts at NIST, Academia & Industry on contributing to Experimental
Quantum Network Science and Metrology utilizing our NIST quantum network testbed in Gaithersburg.
 
Currently, our Quantum Network testbed consists of 4 nodes with more nodes being developed and
added. As part of a metropolitan quantum network, we also are connected to the University of
Maryland campus through a dedicated 60 km dark-fiber link.

This position will focus on the development of critical measurement infrastructure needed provide
reference values that reflect total genomic DNA (gDNA) per sample; total gDNA is critical for applications
including DNA-based detection methods, such as those used in microbiome measurements, and
assessments of DNA extraction efficiency. This approach will focus on quantifying total DNA per sample.
In instances where metagenomics will be applied to whole cell materials, knowing the total amount of
genomic DNA present may be more important than knowing the total number of cells. Having certainty
about the total DNA per sample also allows confident calculation of the per-organism efficiency of DNA
extraction methods.