Research Scientist (Ecological Modelling)

Responsibilities

• Develop and apply mechanistic and trait-based models (e.g., ordinary differential equation: ODE-based systems) to represent climate-sensitive ecological, agricultural, or disease-related processes.
• Integrate climate, environmental, and remote-sensing datasets (e.g., temperature, rainfall, vegetation coverage and productivity indicators) into spatial and temporal modelling frameworks.
• Contribute to the development of hybrid models that combine mechanistic understanding with statistical or data-driven approaches.
• Apply statistical and mathematical modelling techniques, including panel regression and related methods, to evaluate climate impacts, risks, and uncertainties.
• Support analyses related to climate variability, climate change attribution, and scenario-based projections, including medium- to long-term perspectives.
• Develop and apply compartmental models to simulate pest population dynamics, integrating environmental, climatic, and biological parameters to inform risk assessment and guide data-driven, gender-inclusive bio-based pest management strategies.
• Contribute to the integration of outputs from mechanistic and machine learning models into digital advisory tools, enabling real-time decision support for pest management and improving accessibility of risk information for stakeholders.
• Lead resource mobilization efforts by identifying funding opportunities, developing competitive grant proposals, and securing research partnerships to support programmatic and scientific objectives.
• Contribute to peer-reviewed publications, policy briefs, and other knowledge dissemination products.

Requirements

• PhD in Applied Mathematics, Data Science (Environmental or Climate option), Environmental Science (Ecological Modelling), Climate Science, Ecology, or a closely related field.
• At least 4 years of post-PhD research experience in environmental, climate, ecological, and entomological modelling.
• Experience in software development and engineering.

Core Competencies

• Strong grounding in mechanistic modelling, particularly ordinary differential equation (ODE)–based frameworks.
• Demonstrated experience handling and analysing long-term (big) climate and environmental datasets.
• Solid knowledge of statistical and mathematical modelling, including uncertainty analysis.
• Proficiency in R and/or Python for data analysis, modelling, and reproducible research.
• Evidence of scientific productivity through peer-reviewed publications.
• Evidence of resource mobilization through competitive grant proposals.