Background information about the job or company (e.g., role context, company overview)

International Centre of Insect Physiology and Ecology (icipe) is a pan-African Centre of excellence in research for development and capacity building in insect science and its applications. Its mission is to help alleviate poverty, ensure food and nutritional security and improve the overall health status of peoples of the tropics.

Responsibilities or duties

The scientist is responsible for contributing to interdisciplinary, quantitative research at the interface of climate science, environmental systems, and biological modelling in support of icipe’s researchfor-development agenda. The scientist will design and apply mechanistic, trait-based, and hybrid modelling frameworks to assess climate-related risks affecting agro-ecosystems, pests, vectors, and disease transmission systems across Africa, and beyond. The scientist will integrate climate, environmental, and remote-sensing data into spatial and temporal analyses, apply statistical and panel regression approaches to evaluate climate impacts and uncertainty, and support scenariobased and attribution analyses. The scientist will work closely with biologists, epidemiologists, agronomists, climate and social and impact assessment scientists to ensure biological realism and policy relevance, contribute to decision-support products, and engage in capacity building, grant proposal development, and dissemination of research outputs with internal and external partners.

Qualifications or requirements (e.g., education, skills)

PhD in Applied Mathematics, Data Science (Environmental or Climate option), Environmental Science (Ecological Modelling), Climate Science, Ecology, or a closely related field.

Experience needed

At least 4 years of post-PhD research experience in environmental, climate, ecological, and entomological modelling.

Experience in software development and engineering.

Any other provided details (e.g., benefits, work environment, team info, or additional notes)

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

• Develop and apply mechanistic and trait-based models (e.g., ordinary differential equation: ODEbased 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 datadriven, 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.

• Mechanistic modelling (ODE-based frameworks)
• Handling and analysing long-term climate and environmental datasets
• Statistical and mathematical modelling
• Uncertainty analysis
• Proficiency in R and/or Python
• Software development and engineering

• PhD in Applied Mathematics, Data Science (Environmental or Climate option), Environmental Science (Ecological Modelling), Climate Science, Ecology, or a closely related field.