Sort by
Refine Your Search
-
Employer
- Stanford University
- Texas A&M University
- Ohio University
- TTI
- Texas A&M AgriLife
- Texas A&M AgriLife Extension
- The Ohio State University
- The University of Memphis
- University of Maine
- University of Maryland
- University of Nevada, Reno
- University of New Hampshire
- University of New Hampshire – Main Campus
- University of Oregon
- University of Texas at Austin
- University of Washington
- 6 more »
- « less
-
Field
-
background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets
-
datasets, and synthesize results for publication and reporting. Proficiency in programming languages and software commonly used in research, such as Python, MATLAB, R, or GIS tools. Excellent written and
-
, dynamic mapping, mobile application development, spatial data analysis, visualization, and GIS. The Lab conducts interdisciplinary collaborative projects with research partners on campus at the UO, with
-
: · MATLAB · Python · ROS · Computer vision and/or YOLO · Pytorch and/or Tensor Flow · LiDAR · GIS · GNSS receivers Minimum Qualifications: Doctoral degree in Mechanical Engineering, Electrical Engineering, or
-
University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 11 hours ago
geospatial data processing and python programming. Candidates should also have knowledge of optical, lidar, and ground penetrating radar sensing systems and understanding of pavement structures and condition
-
for simulating river network dynamics, such as R, Julia, Python, or GIS-based hydrological modeling platforms. Ability to integrate physical, chemical, and biological components into the river-lake network models
-
in data analytics and statistical methods, particularly using tools such as R, Python, or other relevant software. Experience with Data Visualization & Programming: Expertise in data visualization
-
data Experience with GIS/RS and database environments (e.g., ArcGIS and Quantum GIS) Experience with machine learning and statistical learning Experience working with large, diverse datasets Familiarity
-
technologies and methodologies, and Geographic Information Systems software (e.g., ArcGIS, QGIS), Python, and Matlab is required. Knowledge of other programming languages (e.g., C, C++, R, Javascript) is
-
to offer. Qualifications: Required: PhD in ecology by start date Experience in plant phenology, biogeography, and spatial and temporal modeling (Bayesian and frequentist) Expertise in R or Python, GIS, big