University of California, Berkeley, USA
The Department of Environmental Science, Policy, and Management (ESPM) in the College of Natural Resources at the University of California, Berkeley invites applications for a tenure-track position at the assistant professor level in Quantitative Environmental Remote Sensing with an expected start date of July 1, 2018.
Remote sensing plays an increasingly important role in environmental and social sciences, providing novel ways to understand environmental and earth system dynamics at local and global scales.
The person who fills this position will focus on remote sensing and environmental and earth observation and will have strong quantitative skills. The university encourages applicants whose research integrates remotely sensed measurement with field data in quantitative approaches. Successful applicants might focus on the development of new analytical capabilities, algorithms and classifiers to take advantage of current and new remotely sensed data from both active and passive sensors as integrated with other conventional data types. The university seeks an individual who applies innovative tools and novel approaches with remotely sensed data to meet public demand for science that addresses the environmental challenges facing society.
The successful recruit is expected to develop an internationally recognized research program in remote sensing focusing on environmental and earth observation, large data computation and multi-sensor fusion and synthesis. Possible areas of emphasis for this position would include:
- Data driven discovery, analysis and modeling of trends and dynamics of ecological processes and environmental concerns such as carbon emissions, wildlife, disturbance, land use, productivity and biodiversity.
- Developing and implementing decision support tools for sustainable use of earth observation data for contributing solutions to local and global challenges.
- Estimating biophysical parameters in support of ecological modeling.
- Utilization of remotely sensed data from a range of sources (e.g. multispectral, hyperspectral and active sensors such as Lidar and RADAR).
- Scaling ground-based data from sensor networks with remotely sensed data.
- Development of open source data processing tools for large scale applications.
- New approaches to use remotely sensed data to understand global change.
- Improving classification algorithms through advances in data fusion or new classification algorithms.
The successful candidate will have or be working towards a doctoral degree in a relevant field such as (but not limited to), ecology, forestry, computer science, natural resources or geography. At a minimum, applicants must have completed all degree requirements for the Ph.D. or equivalent degree with the exception of dissertation at the time of application. Ph.D. or equivalent degree is required by the appointment start date.
The successful candidate must have strong quantitative, modeling and simulation skills as applied to environmental spatio-temporal processes. Prior experience in applications of remote sensing and field data collection in land, air and water resources is desirable due to the drought and wildland fire problems that are facing California.
The university is especially interested in candidates who have postdoctoral or equivalent research experience in remote sensing and geospatial analytics with a demonstrated record of research excellence using innovative approaches and broad array of research techniques (e.g., field data collection, data mining, spatio-temporal analysis, object-based analysis, data fusion, etc.)