Dr. Kang Sun’s research group in the Department of Civil, Structural & Environmental Engineering and RENEW Institute at University at Buffalo is seeking a postdoctoral candidate interested in satellite remote sensing of atmospheric composition. Using a combination of satellite, aircraft, and ground-based observational data as well as numerical model simulations, the successful candidate will lead research on 1) deriving surface concentrations of air pollutants based on satellite column observations, 2) retrieving abundance of atmospheric species from remotely sensed spectra, and/or 3) quantifying emissions of atmospheric reactive species and greenhouse gases using observations and models.
Candidates with previous experience in remote sensing of the Earth’s atmosphere, high-performance computing in Unix-based operating systems, and scientific computing languages (Matlab, Python, R, and Fortran) are preferred, but highly motivated candidates with background in meteorology and climatology, hydrology, geography, and earth sciences are encouraged to apply. A Ph.D. degree is required by the time of appointment.
The successful candidate will benefit from close interactions with satellite science teams such as OMI, TROPOMI, TEMPO, and MethaneSAT and collaborators from Boston University, Harvard University, the Smithsonian Institution, NASA, and NOAA.
Position start date is as soon as possible but can be changed depending on the candidate’s availability. The initial contract duration is 12 months. Extension is desired but conditional on the performance of the candidate.
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The candidate will possess a PhD in atmospheric science, environmental engineering, meteorology and climatology, hydrology, geography, or related field. The candidate should demonstrate strong written and oral communication skills through peer-reviewed publication and presentations.
Previous experience in remote sensing of the Earth’s atmosphere, radiative transfer, high-performance computing in Unix-based operating systems, and scientific computing languages (Matlab, Python, R, and Fortran) are preferred.