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Spatial Data Scientist - Machine Learning & Remote Sensing
Overview
Under the supervision of the head of SPACIAL, the Spatial Data Scientist - Remote Sensing will lead and implement advanced remote sensing analysis tasks, including processing of optical and SAR data, timeseries analysis, and predictive modeling with primary focus on the modeling of temporal dynamics across complex landscapes.
The position will support a range of projects and programmes across CIFOR-ICRAF, including Regreening Africa, Knowledge for Great Green Wall Action (K4GGWA), Towards Ending Drought Emergencies (Twende), and upcoming assessments of soil and land health with funding from NORAD.
Key Responsibilities
Key responsibilities include designing and implementing machine learning pipelines for geospatial analysis, developing and deploying deep learning models (CNNs, RNNs, LSTMs, Transformers) for image classification, segmentation, object detection, and time series forecasting.
Qualifications
Qualifications: PhD or MSc degree in spatial data science, geoinformatics, computer science, or a related quantitative field with demonstrated expertise in machine learning and AI applications.
This is a Globally Recruited Staff (GRS) position with a two-year appointment, inclusive of a six-month probationary period, with the possibility of extension. Duty station: Nairobi, Kenya or Remote.