The Disaster Analytics for Society Lab (DASL) brings together students and researchers with a variety of backgrounds and interests who share the common goal of reducing the vulnerability of communities to natural hazards. Some of the expertise relevant to our research includes engineering, computer science, design, statistics, economics, geography, social science, environmental science, earth science, remote-sensing and data analytics.
We believe that research is deeply human work — and our group code of conduct, values, and processes reflect this. We want working with DASL to be as valuable for you as it is for our community of researchers.
Our group brings together students and researchers with a variety of backgrounds and interests who share the common goal of reducing the vulnerability of communities to natural hazards. Some of the expertise relevant to our research includes engineering, computer science, design, statistics, economics, geography, social science, environmental studies, earth science, remote sensing and data analytics.
Support the testing and implementation of machine learning algorithms and their integration within the risk analysis framework.
Create and update code documentation, libraries and advise on best data management practices.
Experience with field research, ability to mentor and work in teams of researchers, and experience working with spatial data. The ideal candidate will combine strong technical expertise with a passion for humanitarian and disaster-related research.
PhD degree in quantitative earth or environmental system sciences, civil / environmental engineering, computer science or related fields.
The RF will support the testing and implementation of machine learning algorithms and their integration within the flood risk analysis framework. They will also develop flood damage models appropriate for select case-study cities
Experience with flood modeling tools and state-of-the-art software (especially the Deltares flood modeling suite), machine learning and developing flood damage models. The ideal candidate will combine strong technical expertise with a passion for humanitarian and disaster-related research.
PhD degree in quantitative earth, environmental system or environmental engineering sciences or related fields.
Pursue cutting-edge research on urgent and important problems in risk analysis, resilience science, and recovery processes. Field-based research is funded and encouraged through our collaborations with partners across Asia.
PhD students are fully funded, including tuition, living costs, housing, conferences, publishing, fieldwork, health insurance, and more. There are also prestigious scholarships and fellowships available to supplement departmental funding.
Courses are taught by faculty at the Asian School of the Environment, and feature multidisciplinary contexts. PhD candidates are encouraged to TA for faculty and develop diverse skillsets.
HOW TO APPLY
Send an email to David with a CV and more information on your interests and qualifications. Click the link below for more information on the program.MORE INFO
The RA will support the planning, coordination and execution of field activities for DASL, leading to successful, safe, and ethical disaster-related fieldwork. They will also support the development of a state-of-the-art spatial analysis lab at NTU.
Experience with field survey techniques and equipment, including unmanned aerial systems, is a plus. Familiarity and interest in research around natural disasters, humanitarian emergencies or other complex field research environments is preferred.
Honours or Master’s degree in Earth Science, Geoinformatics, Geography, Engineering, or other related field.
Conceive, design, prototype and implement solutions to collect and analyze complex data on natural hazards and their impacts on society; test and implement machine learning algorithms in risk analysis frameworks.
Strong understanding of applied statistics methods, preferably (but not necessarily) applied to earth and environmental systems modeling. Experience with statistical programing with R or Python. Knowledge of geostatistics is a plus.
BSc or MSc in statistics, computer science, quantitative earth or environmental system sciences, or another quantitative field.
HOW TO APPLY
Submit a CV, work samples and details of two referees to David at this email:APPLY