This is a remote position.
Spatial data science
- Design and implement machine learning pipelines for geospatial analysis, including feature engineering, model selection, hyper parameter tuning, and validation.
- Develop and deploy deep learning models (CNNs, RNNs, LSTMs, Transformers) for image classification, segmentation, object detection, and time series forecasting.
- Apply advanced AI techniques for predictive modelling and mapping of indicators relevant to ecosystem health assessment using field data and multi-source remote sensing.
- Process and analyze optical data (Sentinel 2, Landsat 8/9) and SAR data (Sentinel 1), including data fusion and feature extraction for ML workflows.
- Implement time series analysis and forecasting models, including trend detection, anomaly identification, and predictive analytics for vegetation, precipitation, and land surface dynamics.
- Develop scalable, reproducible spatial data processing workflows and contribute to MLOps practices.
- 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 on machine learning, AI applications, and spatial data science.
- Capacity development of partners and stakeholders through workshops as part of projects with particular emphasis on ML-driven spatial analysis and modelling.
Stakeholder engagement
- Work closely with the CIFOR-ICRAF stakeholder engagement team (SHARED) to provide AI-driven 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
- 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.
- Proven experience developing and deploying machine learning models for geospatial applications.
- Strong proficiency in deep learning frameworks (TensorFlow, PyTorch, Keras) and familiarity with architectures such as CNNs, RNNs, LSTMs, and Transformers.
- Advanced programming skills in Python and/or R Statistics; familiarity with Julia is a plus.
- Experience with cloud computing platforms (GEE, AWS, GCP) and big data processing tools for geospatial analysis.
- Knowledge of remote sensing data processing and analysis, including optical and SAR platforms.
- Excellent interpersonal skills.
- Excellent written and spoken English. Knowledge of French a plus.