Sort by
Refine Your Search
-
Listed
-
Employer
- University of North Carolina at Chapel Hill
- National Aeronautics and Space Administration (NASA)
- Princeton University
- Stanford University
- Texas A&M AgriLife
- Duke University
- Oak Ridge National Laboratory
- Rutgers University
- The Ohio State University
- University of Florida
- University of Minnesota
- University of New Hampshire
- University of Washington
- Argonne
- Central State University
- Northeastern University
- Texas A&M University
- The University of Memphis
- University of Maryland
- University of Massachusetts
- University of Nevada Las Vegas
- University of North Carolina at Charlotte
- University of Oregon
- University of Texas at Austin
- 14 more »
- « less
-
Field
-
ability to program (e.g, python, R, and/or javascript). Must have at least some experience working with geospatial data. Preferred Qualifications, Competencies, and Experience Ability to develop impactful
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
ability to program (e.g, python, R, and/or javascript). Must have at least some experience working with geospatial data. Preferred Qualifications, Competencies, and Experience Ability to develop impactful
-
area two, which focuses on integration of advanced data science with geospatial technologies to model environmental systems and predict climate impacts. Successful candidates will contribute
-
learning applied to geospatial data Experience with Amazon Web Services or other cloud-based computing platforms Special Instructions to Applicants: For full consideration, applications must be submitted
-
hydrologic connectivity metrics. Furthermore, the qualified candidate must possess advanced skills in geocoding, GIS, raster analysis/processing, and the management of large geospatial datasets. Familiarity
-
expected to have experience in hydrologic/hydraulic modeling and in one or more of the following topics: in situ sensor installation and flood monitoring, GIS & geospatial big data, AI/ML and data science
-
point cloud data processing, deep learning for time series data prediction, digital twin, geospatial mapping with vehicle and UAV mounted remote sensing systems or robotic systems, crowd simulation
-
, Geospatial Science, Data Science, Computer Engineering, or a closely related field, with an emphasis on machine learning, AI, remote sensing, computer vision, or interdisciplinary data science applications
-
. The expected base pay range for this position exceeds Stanford guidelines. Required Qualifications: PhD with substantial expertise in data science, geospatial techniques, and statistical/causal inference
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
emphasis on remote sensing. 2. Experience of using multiple sources of remotely sensed data, particularly optical, Lidar, and Radar data. 3. Sound statistical skills and use of Machine Learning/Geospatial AI