We (Dr. Chong Kwek Yan, A/P Hugh Tan, and A/P Darren Yeo) have a six-year funded project that seeks to develop an index of the biodiversity in urban areas. We are looking for one research staff, preferably a post-doctoral fellow (but see Requirements below), to join the team. The standard contract duration is one year but is renewable contingent upon good performance until the end of the project (estimated to be 30 June 2022).
- Develop an automated workflow to classify urban greenery cover and water bodies from freely available satellite imagery and spatial data sets.
- Analyze the relationships between the urban landscape and field data collected on bird, butterfly, odonate, and anuran communities.
- Lead the research team in project management, including working closely with the collaborating government agency, and drafting project progress reports and slides for and minutes of progress meetings.
- Lead the writing of scientific manuscripts for peer-reviewed journals.
- Mentor undergraduate research students contributing to the research project.
Preferably a PhD with demonstrated experience in landscape/spatial ecology and/or remote sensing projects; or a MSc in Applied Geographic Information Systems, or computer science or computer engineering with demonstrated experience in remote sensing, image classification, and spatial analytics. A BSc (Honours) with an excellent track record in related projects and publications and the relevant skill sets would also be considered.
More importantly, we are looking for following skills and experience:
- Proficiency in open-source scripting languages for image classification, spatial analysis, and statistical analyses, such as R or Python.
- Evidence of excellent writing (in English), including the ability to draft manuscripts for publication in peer-reviewed scientific journals.
- Good project management skills, including time management and attention to detail.
- Ability to work in and potentially lead a small team.
- Good verbal and scientific communication skills, including the ability to work with government agencies.
- Experience working with both freely available as well as commercially acquired high resolution satellite or drone imagery as well as spatial data sets, in particular related to the urban landscape.
- Experience using deep-learning techniques to map elements of urban greenery, such as individual trees, would be advantageous.
- Some experience with field surveys of urban wildlife.