Research Fellow, Machine Learning
The successful candidates are expected to be involved in a project titled ‘Advanced geological mapping using machine learning techniques’ that utilizes large data set of borehole samples.
For research fellow appointment, the successful applicant is expected to have a PhD degree, or be about to complete his or her PhD study in civil/geotechnical engineering, engineering geology, or any other relevant areas to the project (e.g., machine learning, data analytics, statistics, operations research). Specific guidelines are given below:
- Strong background in machine learning, data analytics, and statistics. It is preferred that the individuals demonstrate prior knowledge of applying machine learning techniques for modelling spatial variability and have experience with computation with sizable data sets. Candidates who have no knowledge of geotechnical engineering/geology but who are interested in applying machine learning approaches to geological mapping can apply for this position.
- Good communication and team working skills with the ability to present research findings in publications and meetings are also required. The salary will depend upon research experiences. Only short listed applicants will be notified.
To apply, please email the following documents to Assistant Professor Ku Taeseo at ‘email@example.com’ with cc ‘firstname.lastname@example.org’. Put “Postdoc ML/GE” in subject line of the email. Requested materials include:
- A cover letter;
- Curriculum vitae;
- Two publications if available;
- Contact information for at least three referees.
More information on The Department of Civil and Environmental Engineering can be found at www.eng.nus.edu.sg/cee/;
Information about working and living in Singapore is available at Contact Singapore.
Location: Kent Ridge Campus
Department : Civil And Environmental Engineering
Employee Referral Eligible: No