Job Description
Remote sensing-based biomass estimation has attracted much attention due to the relative ease and low cost of acquiring remotely sensed data over nearly inaccessible forested areas. To date, the AGB estimation over dense tropical rainforest remains challenging. This 1-yr project attempts to improve forest biomass estimation through exploring the applications of machine learning techniques in AGB estimation and the synergy of SAR with satellite lidar data for wall-to-wall AGB mapping. The research also emphasizes on the uncertainty analysis of ESA BIOMASS’s estimation through a full error propagation procedure.
About the institution:
The QS World University rankings place NUS in the top 15 universities in the world and number one in Asia. In the same rankings the Department of Geography is generally ranked among the top 10 Geography programmes globally. Further information on the Department is available at: http://www.fas.nus.edu.sg/geog
The positions are available for up two years. Salary will be commensurate with experience, and it will be aligned with the NUS salary scale. An annual bonus, dependent on performance, is also potentially available, in-line with NUS policy.
Hiring Procedures:
Further enquiries can be directed to A/P Hao TANG. More information can be found at: https://discovery.nus.edu.sg/20197-hao-tang
Interested applicants are invited to submit: i) a letter of interest, ii) a Curriculum Vitae and iii) the contact details for two references via email to hao.tang@nus.edu.sg.
Qualifications
This position requires strong quantitative analysis and statistical skills to explore multiple remote sensing data sets. Key requirements include:
• A PhD degree in Geography, Remote Sensing and Photogrammetry, or a related field
• Excellent programming techniques
• Strong quantitative data analysis (spatial statistics in particular)
• Some fieldwork experience, preferably in the tropics
• Good oral and written communication skills
• Prior experience in publishing in peer-reviewed scientific journals
