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Duties and responsibilities
Spatial data science
- Optical data processing and analysis, including from sensors such as Sentinel 2 and Landsat 8/9.
- SAR data processing and analysis, primarily Sentinel 1.
- Timeseries analysis, including detection of trends and anomalies in temporal data (vegetation, precipitation, land surface temperatures, etc)
- Predictive modeling of time series data, including forecasting where relevant.
- Applications of machine learning for predictive modeling and mapping of indicators relevant to assessment of ecosystem health using a combination of field data and remote sensing.
- Spatial data analysis tasks, including spatial queries, vector data extraction, processing, and cleaning.
- General data analysis and report writing.
- Supervise a team of junior spatial data scientists and developers.
- Develop communication products/outputs where relevant.
Capacity development
- Lead internal capacity development seminars within CIFOR-ICRAF.
- Capacity development of partners and stakeholders through workshops as part of projects with particular emphasis on spatial data processing and modeling.
Stakeholder engagement
- Work closely with the CIFOR-ICRAF stakeholder engagement team (SHARED) to provide analytical outputs that feed into project delivery, for example monitoring outputs as part of the Great Green Wall.
- Contribute to stakeholder engagement events as part of the development of decision support tools and platforms.
Various other tasks
- Contribute to micro-dashboard development as part of the Global Resilience Impact Tracker platform
- Support projects and programs with analytical support and stakeholder engagement with decision makers.
- Lead and/or contribute to scientific papers.
- Contribute to proposal development and writing.
Requirements
Education, knowledge and experience
- MSc degree in remote sensing and spatial data science is required with proven ability to conduct highly complex analytical tasks.
- Excellent interpersonal skills.
- Excellent written and spoken English.
- Advanced computer programming skills in R Statistics, some knowledge of Python and/or Julia, database technologies, and data management.
How to Apply
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