CIRES / Earth Science and Observation Center, Research Scientist in Remote Sensing

CIRES is an internationally recognized leader in innovative environmental science and research and is located at the University of Colorado Boulder. At CIRES, more than 900 environmental science professionals work to understand the dynamic Earth system, including people’s relationship with the planet. CIRES has partnered with NOAA since 1967, and our areas of expertise include weather and climate, changes at Earth’s poles, air quality and atmospheric chemistry, water resources, solid Earth sciences, and more. Our vision is to be instrumental in ensuring a sustainable future environment by advancing scientific and societal understanding of the Earth system.

The Earth Science and Observation Center (ESOC) within CIRES applies satellite and ground-based geophysical observations to problems with the dynamic Earth system. The Center includes about a dozen active teaching and research faculty, and two dozen affiliated post-doctoral researchers and graduate students. There is a strong field component to much of the research.

• Process, analyze and assimilate satellite data and reanalysis data from a wide variety of sensors (SWOT, SAR, optical, precipitation, spectral) for use in characterizing hydrologic and earth surface processes. This includes the development of new algorithms and process workflows for remote sensing techniques. (30%)
• Employ existing models and develop novel approaches to model earth surface processes using existing remote sensing observations. Apply statistical methods, pattern recognition, and machine learning, where applicable. (30%)
• Seek, establish, and nurture collaborations with researchers both internally within CU system (ESOC, CIRES, INSTAAR, campus wide) and externally at other universities and government laboratories (USGS, Army Corps of Engineers, NASA). (10%)
• Write proposals to fund novel research activities and ideas from new and existing research efforts. (10%)
• Effectively convey research results through peer-reviewed journal articles and presentations. (10%)
• Participate in community outreach activities that advance science in the field (manuscript peer reviews, funding agency panel) and convey the scientific achievements of the researcher and the CIRES community as a whole. (5%)
• Perform other duties as necessary (mentor undergraduate and graduate students, various project support). (5%)

• Visa sponsorship is not offered for this position. Candidates needing visa assistance now or in the future, are not eligible for hire in this role. Candidates must be able to legally work in the U.S on a full-time basis by the start date.
• Give project deliverables the anticipated targeted start date is early summer.

• The position requires a PhD in hydrology, oceanography or engineering, or a similar field.
• At least 4 years of remote sensing and hydrology experience.
• At least 1 year of earth surface modeling is required.
• Experience in one or more fields is required: Remote Sensing, Earth Surface Modeling, Earth System Science.

• Ability to consistently promote, work, and act in a manner to support CIRES’s vision, mission and values.
• Expertise in remote sensing processing and applications.
• Knowledge of earth system dynamics and geophysical processes.
• Ability to process large data volumes and sets. Strong skills with data processing and visualization.
• Ability to manage time and prioritize tasks and desire to contribute to a collaborative and diverse work environment.
• Excellent verbal and written communication skills.

Please note that while the position details both required and preferred skills and experience, we invite applicants to apply even if they do not have the preferred skills and experience outlined in this section. If you meet the requirements and have passion for the work, you are encouraged to apply. We encourage on the job training for any additional skills or knowledge that become relevant to the position.

• Expertise in high-level programming languages (Python, MATLAB, R, IDL).
• Experience with hydrologic models and data analysis.
• Familiarity with random and systemic noise and associated error analysis.