Job Openings
Environmental Data Specialist
Job Summary
The Cooperative Institute for Great Lakes Research (CIGLR) is seeking an individual to join our team as an Environmental Data Specialist. This role will focus on the design and development of observing networks for the Great Lakes region, and the maintenance and enhancement of scientific software and applications. The individual will work closely with CIGLR, partners affiliated with the Great Lakes Observing System (GLOS), and the National Oceanic and Atmospheric Administration’s Great Lakes Environmental Research Laboratory (NOAA GLERL) to support research efforts and data management.
This position is open only to US Citizens or permanent residents due to federal security clearance required for access to NOAA GLERL facilities and resources.
Term Limited Appointment: This is a one-year term limited appointment, with an opportunity for extension based on funding and performance.
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Qualifications:
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- A bachelor’s degree or higher in computer science, data science, natural sciences, engineering, or a related field. Master’s degree preferred.
- 1-3 years of related experience in a professional setting.
- Experience working with large datasets in open-source software environments, particularly Python.
- Demonstrated experience in using and developing software systems for scientific applications, particularly for analyses, visualization, and/or quality control. Experience with geospatial data is also preferred.
- Demonstrated ability to work both independently and in a collaborative environment.
- Coursework or practical experience with computational and machine learning packages (e.g., PyTorch, Tensorflow).
- Experience with cloud-based database management and data-driven web pages using APIs.
Application Deadline: February 26, 2025
Hydrologic Data Specialist
Job Summary
We are seeking a Hydrologic Data Specialist to support efforts underway at the Cooperative Institute for Great Lakes Research (CIGLR) and the NOAA Great Lakes Environmental Research Laboratory (GLERL) to develop next generation hydrologic and water level forecasting of the Great Lakes and advance ice data analysis.
In this position, you will:
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- Integrate hydro-meteorological features, such as teleconnections, snow, and soil conditions, into data-driven hydrologic models for the Great Lakes region.
- Assess model performance and integration of models into the operational framework.
- Analyze ice modeling and observation datasets and perform intercomparisons.
- In addition to data analysis and computing work, you will also be expected to attend collaborative meetings and provide progress updates, present results at scientific conferences (e.g., the International Association for Great Lakes Research), and help prepare manuscripts for publication in high-impact journals.
- You will be expected to work as part of an interdisciplinary team, as well as independently on individual job responsibilities and goals. In this position you will report to SEAS-CIGLR Assistant Research Scientist, Yi Hong.
This position is open only to US Citizens or permanent residents due to federal security clearance required for access to NOAA GLERL facilities and resources.
Term Limited Appointment: This is a one (1) year term limited appointment, with an opportunity for extension based on funding and performance.
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Qualifications:
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- A Bachelor’s degree in a field similar to hydrology, Earth-system data science, geosciences, or climate science. A Master’s degree is preferred.
- At least 1 year of related professional or academic experience.
- Experience working with machine learning or AI models.
- Familiarity with hydrologic numerical modeling.
- Familiarity with data analysis and visualization in a scripting environment using Python or similar software.
Application Deadline: February 26, 2025
Student Fellowships
CIGLR offers two fellowship programs that support student research opportunities, in order to train the next generation of Great Lakes researchers. Click the button to learn more about current and upcoming student fellowship opportunities.