The Climate Transformation Programme (CTP) aims to develop, inspire and accelerate knowledge-based solutions and educate future leaders to establish the stable climate and environment necessary for resilient, just, and sustainable Southeast Asian societies. CTP will generate knowledge and innovation across disciplines including climate and Earth science, ecology, materials science, artificial intelligence, humanities, social sciences, the arts, finance, health, and engineering. It will also translate state-of-the-art scientific results into real world solutions for Singapore and transfer these solutions to Southeast Asia (SEA) and beyond. As part of this programme, Cross Cutting Theme 2: Satellite Remote Sensing will map and monitor the impact and solutions of climate change in Southeast Asia region.
The Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS) at Nanyang Technological University in Singapore invites applications for a Research Fellow / Scientist / Engineer to support CTP Cross Cutting Theme 2 (Satellite Remote Sensing). The EOS-RS teams’ research covers SAR, multi/hyper-spectral remote sensing, LiDAR, and GNSS data for disaster response and hazard monitoring for earthquakes, volcanoes, and impacts of climate change.
EOS is a University Research Institute at Nanyang Technological University that conducts fundamental research on earthquakes, volcanic eruptions, tsunamis, and climate change in and around Southeast Asia, towards safer and more sustainable societies. For more information on EOS please visit http://www.earthobservatory.sg.
EOS-RS and CTP welcomes applications from qualified candidates who have interests and expertise relevant to the use of remote sensing for mapping, monitoring, and understanding the causes and effects of climate change. We are particularly interested in candidates who have expertise in analysing and interpreting satellite remote sensing data from Synthetic Aperture Radar (SAR) and multi/hyper-spectral sensors for measuring ground deformation, mapping surface changes due to disaster events, and mapping ocean colours and ocean topography for carbon flux estimates. We are also interested in candidates who have experience applying machine learning to InSAR and other remote sensing observations.
Successful candidates will have a Master’s/PhD in a relevant field (e.g., earth science, electrical engineering, image processing, and deep learning), expertise in processing and modelling geodetic data using open-source analysis tools, an ability to apply AI/ML techniques to remote sensing data; and demonstrated track record of impactful research or projects in topics related to the position. They will also have strong English and communication skills, the ability to write code using Python or MATLAB, the desire and ability to work as part of a multicultural team, and a demonstrated ability for creating intellectual value.
Additionally, the successful candidate will be part of the large and interdisciplinary research teams at EOS, the Asian School of the Environment (ASE), and the School of Electrical and Electronic Engineering (EEE), helping to develop algorithms and systems for disaster mapping and climate change in collaboration with space industries and responding agencies around the world. They will collaborate with the geodesy group and many other groups at EOS/ASE.
Key Responsibilities:
- Architect and develop InSAR-based remote-sensing solutions and capabilities grounded in current research to address needs of scientists and external stakeholders
- Coordinate and manage application development and experimentation efforts across projects and synergies
- Conduct feature experimentation and develop applications both independently and in a team
- Help to mentor Research Assistants/Associates
Job Requirements:
- Master’s/PhD in Earth science, geophysics, geodesy, physics, electrical engineering, natural hazards, computer science, machine learning, or a related field.
- Demonstrated expertise in processing SAR data (with open-source tools such as ISCE or GMTSAR) for surface deformation measurement and change mapping.
- Strong technical skills in data processing for remote sensing applications; experience in optimizing and implementing automated processing pipelines (in cloud, HPC environments) for multi-modal, large remote-sensing datasets for machine and deep learning desired.
- Evidence of the ability to write and present at the level required for peer-reviewed scientific journals and international conferences, which will usually be in English.
- Evidence of independence in research direction and team spirit.
Interested applicants should submit a CV, list of publications, research statement, diversity and inclusivity statement, and the names and contact information for three referees. For additional information please email Assoc. Prof. Sang-Ho Yun at [email protected]. Applications will be reviewed on an ongoing basis.
EOS seeks a diverse and inclusive workforce and is committed to equality of opportunity. We welcome applications from all and recruit on the basis of merit, regardless of age, race, gender, religion, marital status, family responsibilities, or disability.
We regret to inform that only shortlisted candidates will be notified.
