Assistant Research Professor/Post Doc Associate
- Employer
- University of Maryland Department of Geographical Sciences
- Location
- College Park, MD
- Salary
- Salary and benefits are highly competitive.
- Closing date
- Nov 4, 2022
View more
- Discipline
- Earth and Space Science Informatics, Global Environmental Change
- Career Level
- Experienced
- Education Level
- PhD
- Relocation Cost
- No Relocation
- Sector Type
- Academia
Job Details
The Department of Geographical Sciences at University of Maryland, College Park, is seeking multiple Professional Track Research Faculty Members (non-tenure positions) at either the Post-Doctoral Associate rank or the Assistant Research Professor rank, commensurate with qualifications and experience. Salary and benefits are highly competitive. These positions will cover a range of activities, including support of the Global Ecosystem Dynamics Investigation – GEDI (https://gedi.umd.edu/), other research projects, as well as new research proposed by the applicant.
GEDI data support research projects at UMD in the areas of biomass estimation, habitat characterization, and prognostic ecosystem models, among others. GEDI data are also being used in research projects specifically targeting their fusion with our remote sensing data, including passive optical, lidar, SAR and SAR-interferometry, such as NASA’s NISAR and ICESat2 missions, and DLR’s TanDEM-X mission. The GEDI mission is expected to continue making observations in 2022 with final mission data products at the end of FY2024, while other associated research projects have timelines that extend beyond this date. Candidates will contribute to various aspects of GEDI and allied science analyses, including refinement and validation of science algorithms, post-flight calibration and validation, development of field observation data bases, science data product development, data, multi-sensor data fusion, including SAR remote sensing.
Applicants should have a background in disciplines aligned with Earth observation and terrestrial ecology, including interests in remote sensing science, machine learning, ecosystem structure and biomass, ecosystem modeling, habitat/biodiversity studies and related areas. Applicants are encouraged to develop their own research interests in these areas, leveraging the resources and the University of Maryland, NASA Goddard Spaceflight Center (GSFC) and others. Technical expertise in lidar or SAR remote sensing is highly desirable; however, applicants with strong experience in other areas of remote sensing, or in application of machine learning to remote sensing are also encouraged to apply.
The researchers will be located at the new International Center for Innovation in Earth Observation and Geospatial Analysis at the University of Maryland but may also spend time nearby at NASA Goddard Spaceflight Center. There may also be periods of fieldwork, both domestic and foreign, in support of research products and goals.
Minimum Qualifications: An earned doctoral degree in Geographical Sciences or allied fields in environmental science, such as Biology and Forestry is required. Candidates with doctoral degrees in other fields (for example, Physics, Computer Science, and Electrical Engineering) with a demonstrated knowledge and understanding of remote sensing of the land surface will also be considered. Competent programming and statistical skills (e.g., such as Python, IDL, MatLab, C/C++, R, TensorFlow) are required. Candidates at the level of Assistant Research Professor must have a demonstrated ability to independently conduct research as evidenced by peer-reviewed publications.
Further information on the research programs of the Department may be obtained from the address above and can be found at http://www.geog.umd.edu.
Company
The Department of Geographical Sciences at the University of Maryland College Park has a mission to make fundamental contributions to the advancement of geographic science. A look at the department's contributions through teaching, research, and more are shown in https://youtu.be/aORoA-cgCkw
- Website
- https://geog.umd.edu/
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