The Research Associate (Field & Lab Coordinator) will join a dynamic, expanding research group in Asian School of the Environment (ASE) that investigates the complex impact that natural hazards have on society. In particular, the selected candidate will coordinate the field and lab activities of the group. Active participation in research will be encouraged, and support for publication and meeting/conference presentations can be available.
The ideal candidate will combine strong technical expertise in geospatial analysis with a passion for humanitarian and disaster-related research, a collaborative and team-oriented spirit, and strong project management skills.
- Support in planning, coordination and execution of field activities for the Disaster Analytics for Society Lab (DASL), leading to successful, safe, and ethical disaster-related fieldwork
- Support the development of the spatial analysis lab (hardware, software, and capacity-building in spatial analysis)
- Assist with field/lab's budget planning, forecast, review, management, and spending, including procuring state-of-the-art equipment for deployment in field research
- Conduct spatial analysis work in support of various research projects on disaster risk and recovery
- Willing to travel frequently, occasionally for extended periods (up to 2-3 weeks), especially to countries within Southeast Asia
Strong candidates for this position will have
- An Honors and Master’s degree in Geoinformatics, Engineering, Remote Sensing and GIS, Computer Science, Earth Sciences, Geography, Environmental Studies, or related fields
- Strong technical skills in GIS and spatial analysis techniques, especially in latest tools & technologies for analysis
- Experience with field survey techniques and equipment, ideally in UAV systems and ground-truth data collection for spatial analysis
- Strong organizational and project management skills including managing budgets, procurements, monitoring, training, and deployment planning
- Excellent oral and written communication skills in English
Excellent candidates for this position will also have
- Experience or interest in machine learning, especially as applied to spatial data in disaster risk/recovery contexts
- Familiarity with and interest in human-environmental systems, natural hazards and disasters, and humanitarian or development work
- A strong sense of drive and a collaborative spirit, with interest to work with other researchers and scientists (as a team player) to produce high-quality and impactful research
Please note that only shortlisted candidates would be notified.