Headquartered in Nairobi, Kenya, icipe was established in 1970, to address this very paradox of insects and accompanying challenges. The Centre’s mission is to use insect science for sustainable development, to ensure food security and improve the overall health of communities in Africa by addressing the interlinked problems of poverty, poor health, low agricultural productivity and environmental degradation. Since its founding, icipe has remained committed to developing and disseminating environmentally safe, affordable, acceptable and accessible tools and strategies to control insect pests and disease vectors. icipe’s mandate further extends to the sustainable conservation and utilisation of Africa’s rich biodiversity. icipe is the only international institution in Africa working primarily on arthropods. Therefore capacity building of individual researchers and institutions in Africa is an integral part of all the Centre’s research and development activities. icipe also aims to empower women, harness the youth and build capacity to use, transfer and teach the Centre’s technologies. How we work

Overall purpose of the job

The Postdoctoral Research Fellow is expected to develop key economically important crop pests models linking pest infestation, and climatic variables, and integrate the model into the VIPS – a pest prediction platform to guide preparedness and intervention strategies in selected cropping systems (maize, tomato, etc.) in Malawi.

The successful candidate will:

• Conduct, conceptualise, and develop rule-based models using artificial intelligence (AI) approach based on human reasoning and logic.
• Establish gridded-based agroecosystem spatial modelling for effective conception and selection of adequate pest management methods.
•  Develop and integrate crop infestation model under climate variability to VIPS platform for integrated pest management (IPM) in Malawi

Specific duties

The successful candidate will undertake the following activities in target projects:

•  Conduct research with a focus on a rule-based model for pest monitoring in Malawi.
•  Harnessing pests infestation data with climate-related information.
•  Develop traffic light pest infestation models using rules extracted from pest bioecology.
•  Develop, test, evaluate, and aggregate models related to crop pests and climate.
•  Integrated pests-climate related models to VIPS.
•  Develop tools and techniques to assist in spatially deploying IPM strategies that can lead to prevent crop production losses and ensure minimal insect pest and weed attacks for higher food production.
•  Contribute to the development of digital plant health services.
•  Actively engage in resource mobilisation and proposal development.
•  Contribute to the overall DMMG Unit workplan and strategy.

Requirements

Qualifications

•  PhD in environmental science or any related field with a focus on the spatial use of data science and computer intelligence in insect pests modelling.
•  Extensive knowledge of and experience in artificial intelligence-related modelling, with a focus on advanced, rule-based model such as Fuzzy logic.
•  Experience with data-driven AI models, and crop yield mapping at scale.
•  Experience with advanced data collection and display technologies such as augmented reality and internet of things (IoT).
•  Extensive knowledge of and experience in spatial analysis of invasive insect pests and nature-based solutions.
•  Extensive knowledge of and experience in developing grided-based mechanistic models such as cellular automata.
•  Experience in developing software and web-based platforms to predict insect dispersal at scale.
•  Excellent publication record (at least 3 peer-reviewed publications).
•  A high degree of organisation, adaptability, and prioritisation.
•  Experience in capacity building through mentorship of MSc and undergraduate students.
•  Excellent communication skills.

Core competencies

•  Demonstrated advanced experience using R and Python programming for rule-based models.
•  Demonstrated experience in crop insect climate-related modelling.
•  Experience in big ecological and remote sensing data mining, preprocessing, and analysis for crop pest spatial modelling.

Reporting
This position reports to the Head of the Data Management Modelling and Geoinformation (DMMG) Unit.