At ofi, we’re shaping the future of food, working closely with farming communities worldwide to source and produce high-quality cocoa, coffee, dairy, nuts, and spices. With operations across :50 countries, we combine global scale with deep local roots to deliver trusted, traceable ingredients while creating positive impact for people and planet.

As one of the world’s leading cocoa processors, we connect farmers to global markets through transparent, deforestation-monitored supply chains and long-term sustainability programs, helping build a better future for cocoa and the communities behind it.

Our purpose to be the change for good food and a healthy future guides everything we do. Here, you’ll find driven minds, open collaboration, curiosity to innovate, and a shared commitment. Whoever we’re with, whatever we’re doing, we always make it real.

Background

Cocoa is one of the most important agricultural commodities in West Africa, with Côte d’Ivoire producing more than 40% of the world’s supply. Accurate mapping of cocoa farms is essential for improving supply-chain transparency, supporting zero-deforestation commitments, and ensuring sustainable sourcing. However, cocoa landscapes are highly heterogeneous, complex, and often difficult to distinguish from other tree crops using traditional machine learning approaches. The need for high-resolution spatially explicit cocoa maps that generalize across ecological zones has never been greater.

Remote sensing has long supported commodity-crop monitoring, but conventional methods such as random forests or standard deep learning classifiers require large amounts of labelled data and often struggle to capture fine-scale structural variations in cocoa agroforestry systems. Recent advances in retrieval-augmented generation (RAG) offer a promising new direction. RAG enables models to dynamically retrieve relevant training examples, auxiliary information, or contextual embeddings during inference, improving classification accuracy and generalization, particularly when class boundaries are ambiguous.

In this internship, the student will explore how RAG-enhanced machine learning can improve cocoa farm mapping compared to existing traditional approaches. The student will have access to a large, proprietary database of georeferenced cocoa farms in Côte d’Ivoire provided by ofi, enabling the development, evaluation, and validation of next-generation cocoa-mapping models.

Objectives & research questions

This internship will address the following core questions:

How can retrieval-augmented generation (RAG) improve the classification and mapping of cocoa farms using satellite remote sensing?
How do RAG-enhanced models compare with traditional machine learning approaches (e.g., Random Forest, CNNs, Transformers) when applied to heterogeneous cocoa agroforestry landscapes in Côte d’Ivoire?
Does integrating retrieved contextual information, reference parcels, or learned embeddings improve generalization across ecological zones and planting systems?
What are the implications of these improvements for large-scale monitoring and sustainability reporting in the cocoa supply chain?

Expected activities and deliverable

During the internship you will:

Preprocess and harmonize Sentinel-1, Sentinel-2, and ancillary data for model training
Explore RAG architectures for geospatial classification (e.g., embedding retrieval, context augmentation)
Train and evaluate ML models using the ofi cocoa farm polygon database
Benchmark RAG-enhanced models against traditional machine learning baselines
Produce spatially explicit cocoa distribution maps for Côte d’Ivoire
Document model performance, uncertainty, and generalization across regions
Present results to forecast and sustainability teams of ofi

What You'll Bring

This internship is ideal for a student who:

Is an MSc student in the final phase of their studies (e.g. Remote Sensing, Geomatics, Data Science, AI, Environmental Sciences or similar)
Has a strong interest in geospatial data, machine learning and sustainability
Has experience with Python and geospatial workflows (e.g. Google Earth Engine, QGIS, GDAL, Rasterio)
Is familiar with deep learning frameworks such as PyTorch or TensorFlow
Is curious about advanced ML concepts such as representation learning, transformers or RAG
Can work independently and manage complex datasets and workflows
Is comfortable working with confidential information and willing to sign a Non-Disclosure Agreement (NDA)

What You'll Gain

Hands-on experience with cutting-edge machine learning and RAG techniques
Work directly with one of the largest cocoa polygon datasets and develop skills in large-scale geospatial analytics applicable to industry and research
Contribute to real-world sustainability goals in the cocoa supply chain and opportunity to co-author internal reports
Receive a competitive internship fee for the duration of your placement, recognizing your contribution to this innovative project
Undertake your internship starting as soon as possible, with a minimum duration of four months and the possibility to extend by two additional months, offering flexibility to deepen your experience
Benefit from experiencing different environments, allowing you to collaborate both in the Amsterdam EU Head-office or the factory in Koog aan de Zaan.

ofi is an equal opportunity employer and values diversity. All qualified applicants will receive consideration for employment without regard to racial or ethnic origin, color, age, religion or belief, sex, nationality, disability, sexual orientation, gender identity, gender expression, genetic information, or any other characteristic protected by applicable law.

Applicants are requested to complete all required steps in the application process including providing a resume/CV in order to be considered for open roles.

ofi

Vacatures

Internship: Cocoa Farm Mapping in Côte d'Ivoire with Remote Sensing, ML & RAG