NIST EL – 73 has a new PREP research opportunity described below and in the attached. The NIST Sponsor is seeking candidate recommendations.
OU: EL – 73
Div: 730
Group: Applied Economics Office
Level of Appointment: grad
Schedule of Appointment: part time
Total Hours per week: 20
Work Location: Physically at NIST (Gaithersburg, MD), Remote, Telework
Requested Start Date: 2024-01-01
End Date: 2024-12-31
This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP)
program. NIST recognizes that its research staff may wish to collaborate with researchers at academic
institutions on specific projects of mutual interest, thus requires that such institutions must be the
recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on
scientific research in many areas. Employees in this position will perform technical work that underpins
the scientific research of the collaboration.
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 EL’s Heat Transfer and Alternative Energy Systems (HTAES) Group that has
expertise in PV electrical and opto-electronic characterization and optical metrology and operates a
world-class hyperspectral imaging system and DIP experts from the Information Technology Lab (ITL)
Information Systems Group.
We are looking for a highly motivated graduate student to join our multidisciplinary team developing
measurement science and tools for evaluating solar panel degradation. The ideal candidate will have a
background in machine learning, specifically digital image processing, to evaluate images of solar panels
to estimate solar sell degradation and compare the visual degradation to measured performance data of
the solar panels.
The work will entail:
Key responsibilities will include but are not limited to:
• Developing a quantitative approach to evaluate solar panel degradation using images collected
in the laboratory
• Compare the visual degradation to performance measurements from the laboratory
• Replicate the quantitative approach to in situ solar panel images
• Document code
• Draft documentation of the developed process
• Assist with additional machine learning related projects as assigned
Qualifications
Please upload the following (preferably in a single PDF) with your application:
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PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities
to establish a collaborative research relationship between NIST and U.S. institutions of higher education
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