Santa Barbara, CA
Position Title: Postdoctoral Scholar in Climate Hazard Center
Location: Santa Barbara, CA
Organization Overview: The Climate Hazard Center’s mission aligns with the University of Santa Barbara, California in their shared commitment to public service, which manifests through the creation and distribution of knowledge that advances the well-being of the global community. They improve the early detection and forecasting of hydroclimatic hazards related to food security, droughts, and floods while empowering decision-makers by providing improved climate analysis tools, data sets, and the on-ground support of CHC-affiliated field scientists. They aim to strengthen international disaster risk-reduction efforts by advancing drought early warning science.
As an official Center, they hope to increase our reach and visibility to educate an engaged public on the devastation caused caused by climate disasters. By spotlighting climate hazards, they believe that public awareness of the necessity of timely scientific research and the development of operational techniques that quickly identify and quantify food insecurity will drastically increase.
Position Overview: The Climate Hazards Center at the University of California, Santa Barbara seeks a highly motivated postdoctoral researcher for an exciting project supported by the US Geological Survey. The project focuses on using forecasts of climate (e.g. rainfall, evaporative demand) and hydrologic (e.g. soil moisture) variables, as well as remotely sensed datasets, and statistical methods such as machine learning to predict agricultural statistics (crop production, crop yields, prices) in food insecure countries and will directly support the famine early warning efforts of USAID’s Famine Early Warning Systems Network (FEWS NET). The project will have a strong focus on applications and will leverage cutting edge science to support lives and livelihood saving early warning information.
The University is especially interested in candidates who can contribute to the diversity and excellence of the academic community through research, teaching, and service as appropriate to the position.
Basic qualifications (required at time of application)
- Applicants must have completed all requirements for a PhD Degree in Statistics, Geography/Remote Sensing, Agricultural Economics, Agronomy, Hydrology, Environmental science, Earth Science or a related discipline except the dissertation at the time of application.
Additional qualifications (required at time of start)
- PhD awarded by the time of appointment.
- Strong expertise in handling, processing and visualizing large spatial vector and gridded, datasets (e.g. remotely sensed and climate/hydrologic forecasts)
- Strong background in applied statistical modeling and machine learning
- Experience with processing and analyzing climate forecasts is strongly preferred. Past experience with downscaling and bias-correction of climate forecasts is desired.
- Ability to implement predictive models (including but not limited to machine learning algorithms) based on gridded datasets (e.g. remotely sensed or climate/hydrologic forecasts, time series, and/or panel datasets).
- Fluency in programming languages such as R or Python.
- Demonstrated ability to make reproducible code for cleaning, integrating, and modeling spatial/temporal data from multiple sources, spatial scales, and temporal frequencies.
- Interest and experience in applied sciences
- Proven record of independently leading research projects, conducting reproducible research, publishing journal articles and presenting at international and national conferences.
Application Deadline: February 15, if not filled: June 30
How to Apply: Please use the online application to view the full job description as well as how to apply.