PREP Research Associate - Graduate Computer Science


September 15, 2023

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 high 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


  • Graduate student in Computer science, Software engineering, Programming, or related field
  • Proficient with Python or Java
  • Familiarity with MatLab
  • GPU programming, data visualization or AI experience a plus
  • Logical thinking and problem solving
  • Attention to detail
  • Strong oral and written communication skills

Apply Here

The university is an Equal Employment Opportunity/Affirmative Action employer that does not unlawfully discriminate in any of its programs or activities on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity or expression, or on any other basis prohibited by applicable law.