Overview

Under the supervision of the head of Spatial Data Science and Applied Learning Lab (SPACIAL), the International Scientist – Restoration of Rangeland Health and Earth Observation will work as part of the Restore4More project, leading research on restoration rangeland health in the nexus of biodiversity, water, and climate. The primary geographical focus will be East Africa, but with contributions to work being conducted across rangelands globally. The Restore4More project works towards large-scale, long-lasting, and effective land restoration in drylands for increased climate change adaptation and mitigations, and water and food security. This position is critical for this agenda and will part of a transdisciplinary team. Key aspects of the role will be to lead a unique combination of structured field data collection at scale and experiments, including the use of earth observation, modelling, and participatory action research. The position will also supervise scientists, interns, and students.

Duties and responsibilities

• Conduct transdisciplinary research with key stakeholder groups using a co-learning and knowledge exchange approach. This will include using a unique combination of field data and experiments, earth observation, modelling, participatory action research and mixed methods.
• Contribute to on-going projects across the global CIFOR-ICRAF portfolio with research design, data analysis and development of scientific publications.
• Contribute to the development of decision support systems such as interactive dashboards and online platforms.
• Represent CIFOR-ICRAF at conferences and/or meetings from a technical remote sensing / data science perspective.
• Supervise scientists, students and interns as needed.

Requirements

• PhD in ecology, soil science, water science or related field.
• Minimum 3 years of research experience.
• Background in spatial data science and earth observation.
• Some experience in machine learning and AI is a plus.

Education, knowledge and experience

• PhD in ecology, soil science, water science or related field.
• Minimum 3 years of research experience.
• Background in spatial data science and earth observation.
• Some experience in machine learning and AI is a plus.