Research Assistant / Associate (Atmospheric dispersion modelling)

The Atmospheric Dispersion Team in SNRSI focuses on prediction and quantification of radioactive pollutant dispersion through the atmosphere. The team has been working on long-range dispersion problems and will now be looking at short-range ones. As a team member you will need to design and run the simulation of the transport and deposition of radioactive pollutants; to evaluate the accuracy and uncertainties of the simulations, as well as to analyse and summarise possible impacts and their likelihood under different scenarios (season, weather type, urban structure etc.). Through the project we expect to gain a good understanding of the atmospheric dispersion and deposition processes of radioactive pollutants in Singapore. The ultimate goal is to build the capability and confidence to describe the full pathway of radioactive pollutants over the region of Singapore, a coastal city with water bodies, forests and diversified urban zoning.

Responsibilities include, but are not limited to:

• Conduct research on atmospheric dispersion modelling, risk assessment of radioactive pollutants and model evaluation; deepen the understanding of physics processes and model limitations & uncertainties;
• Develop methods to improve model performance; keep track of the latest research advances in the community;
• Contribute to proposal writing, provision of solution for stakeholders, and any other required tasks;
• Publish paper in peer-reviewed journals; present research work in conferences.

Interested applicants should prepare to request for 3 letters of reference.

• MSc or BSc (with relevant research experience) in atmospheric science, physics, statistics, or mathematics;
• Solid knowledge of physics, maths, and statistics;
• Strong analytical and problem-solving skills, result-oriented and able to think out-of-box;
• Bearing good sense of responsibility, able to deliver and achieve targets on schedule;
• Good written, verbal and interpersonal skills;
• Proficient with Python programming for data processing and visualisation;
• Familiar with UNIX/Linux OS and shell scripting;
• Familiar with FORTRAN and implementation of libraries from source code is an advantage;
• Publications in peer-reviewed journals or relevant research project experience are desirable;
• Experience in numerical weather/ocean modelling is a plus.

At NUS, the health and safety of our staff and students are one of our utmost priorities, and COVID-vaccination supports our commitment to ensure the safety of our community and to make NUS as safe and welcoming as possible. Many of our roles require a significant amount of physical interactions with students/staff/public members. Even for job roles that may be performed remotely, there will be instances where on-campus presence is required.

Taking into consideration the health and well-being of our staff and students and to better protect everyone in the campus, applicants are strongly encouraged to have themselves fully COVID-19 vaccinated to secure successful employment with NUS.