The International Institute of Tropical Agriculture (IITA) is seeking to engage the services of an Individual Consultant to support the refactoring, harmonization, and advanced integration of modelling workflows as part of its broader commitment to strengthening data-driven agricultural research. In line with the Institute’s efforts to enhance digital innovation and improve analytical rigor across programs, this assignment focuses on optimizing existing modelling pipelines to improve computational efficiency, code modularity, interoperability, and scalability. As IITA continues to expand its use of advanced modelling tools for agronomic analysis and research-for-development delivery, strengthening the integration of emerging AI-enabled analytical frameworks has become essential for enhancing precision, forecasting capacity, and decision support across agricultural systems.

The purpose of this assignment is to provide technical expertise to reorganize and standardize modelling scripts into generic, modular, and reusable components, while ensuring tighter coupling between modelling workflows and geospatial information systems. The assignment is critical to advancing IITA’s capacity to generate robust agronomic insights and to assess biophysical, economic, and nutritional outcomes within smallholder farming contexts.

In line with this, we invite suitably qualified and experienced individuals to express their interest in providing the required services.

Scope of Work / Key Responsibilities

The consultant will be responsible for, but not limited to, the following:

• Produce fully refactored and modularized machine‑learning modelling scripts with improved architecture, abstraction, and parameterization.
• Optimize codebases by removing redundant routines and enhancing computational performance and reproducibility.
• Generalize modelling workflows to support multi‑crop, multi‑location, and multi‑scenario applications.
• Integrate empirical modelling frameworks to strengthen predictive performance and model robustness.
• Incorporate additional spatial covariates to improve explanatory power and computational efficiency.
• Prototype AI‑enabled or agent‑based agronomic decision‑support workflow integrations to enhance reasoning, automation, and recommendation quality. 
• Develop comprehensive technical documentation, including annotated code, methodology notes, and workflow descriptions.
• Prepare periodic progress reports reflecting milestones and technical achievements. 

Requirements

Required Qualifications and Experience

• Advanced degree (master’s or Ph.D.) in Agricultural Systems Modelling, Environmental Modelling, Data Science, Artificial Intelligence or any related discipline.
• Minimum of five (5) years of relevant professional experience, preferably in forest carbon assessment or ecosystem monitoring.
• Demonstrated expertise in GIS, remote sensing, carbon accounting and other quantitative methods.    
• Proven track record of scientific publications in forest inventory and tree carbon assessment.
• Experience mentoring graduate students, facilitating training, and contributing to capacity building.
• Strong analytical, communication, and scientific writing skills.
• Previous experience working with international organizations, donor-funded projects, or research institutions will be an added advantage.

Duration and Location of the Assignment

The consultancy will be implemented over an agreed period aligned with the technical workplan and milestones. The assignment may be executed remotely, with regular technical coordination meetings as required.

Application Procedure

Interested individuals are invited to submit the following documents in response to this EOI:

1. A detailed Curriculum Vitae (CV) highlighting relevant qualifications and experience.
2. A brief technical statement (maximum 2 pages) outlining understanding of the assignment and proposed approach to achieving the deliverables.
3. Financial proposal indicating the consultant’s expected professional fees.

Benefits