This is a remote position.

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  Design and implement machine learning pipelines for geospatial analysis, including feature engineering, model selection, hyper parameter tuning, and validation.
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  Develop and deploy deep learning models (CNNs, RNNs, LSTMs, Transformers) for image classification, segmentation, object detection, and time series forecasting.
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  Apply advanced AI techniques for predictive modelling and mapping of indicators relevant to ecosystem health assessment using field data and multi-source remote sensing.
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  Process and analyze optical data (Sentinel 2, Landsat 8/9) and SAR data (Sentinel 1), including data fusion and feature extraction for ML workflows.
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  Implement time series analysis and forecasting models, including trend detection, anomaly identification, and predictive analytics for vegetation, precipitation, and land surface dynamics.
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  Develop scalable, reproducible spatial data processing workflows and contribute to MLOps practices.
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  Supervise a team of junior spatial data scientists and developers. • Develop communication products/outputs where relevant.

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  Lead internal capacity development seminars within CIFOR-ICRAF on machine learning, AI applications, and spatial data science.
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  Capacity development of partners and stakeholders through workshops as part of projects with particular emphasis on ML-driven spatial analysis and modelling.

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  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.
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  Contribute to stakeholder engagement events as part of the development of decision support tools and platforms.

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  Contribute to micro-dashboard development as part of the Global Resilience Impact Tracker platform
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  Support projects and programs with analytical support and stakeholder engagement with decision makers.
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  Lead and/or contribute to scientific papers.
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  Contribute to proposal development and writing.

Requirements

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  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.
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  Proven experience developing and deploying machine learning models for geospatial applications.
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  Strong proficiency in deep learning frameworks (TensorFlow, PyTorch, Keras) and familiarity with architectures such as CNNs, RNNs, LSTMs, and Transformers.
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  Advanced programming skills in Python and/or R Statistics; familiarity with Julia is a plus.
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  Experience with cloud computing platforms (GEE, AWS, GCP) and big data processing tools for geospatial analysis.
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  Knowledge of remote sensing data processing and analysis, including optical and SAR platforms.
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  Excellent interpersonal skills.
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  Excellent written and spoken English. Knowledge of French a plus.