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.
