Environmental ML Data Analyst (Part-Time)

New Jersey Institute of Technology

Town of Newark (WI)

Vor Ort

USD 34.000 - 69.000

Teilzeit

Vor 7 Tagen
Sei unter den ersten Bewerbenden

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Zusammenfassung

New Jersey Institute of Technology invites applications for an Hourly Data Analyst (part-time) at the Center for Natural Resources. The role supports the REACH project, evaluating machine learning approaches and applying deep learning techniques to storms across watersheds.

The successful candidate will forecast water level and streamflows, provide progress reports to Dr. Boufadel, and collaborate with scientists and engineers. This position offers flexible hours and unique research exposure.

Qualifikationen

  • PhD in Computer Science, Data Science or equivalent or related disciplines.
  • Experience working with research centers, government-funded environmental projects (e.g., NJDEP, EPA, NOAA).
  • Experience with application of machine learning in Environmental projects.
  • Experience collaborating with scientists, engineers, and community partners on outreach initiatives.

Aufgaben

  • Evaluate various machine learning approaches.
  • Implement techniques for deep learning as applied to storms over different watersheds.
  • Forecast water level and streamflows at various locations.
  • Provide progress reports to Dr. Boufadel.
  • Performs other related duties as assigned or requested by the Center Director.

Kenntnisse

Machine learning
Deep learning
Communication

Ausbildung

PhD in Computer Science, Data Science or equivalent

Jobbeschreibung

New Jersey Institute of Technology invites applications for an Hourly Data Analyst (part-time) at the Center for Natural Resources. The role supports the REACH project, evaluating machine learning approaches and applying deep learning techniques to storms across watersheds.

The successful candidate will forecast water level and streamflows, provide progress reports to Dr. Boufadel, and collaborate with scientists and engineers. This position offers flexible hours and unique research exposure.

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