Research Fellow (Physics/Geophysics/Atmospheric Sciences/Oceanography)

National University of Singapore; Singapore; On-site; SGD 60,000 - 120,000; Full time

Job summary

The National University of Singapore invites applications for Research Fellow positions within SNRSI, focusing on radionuclide dispersion modelling and environmental impact assessment in Singapore and beyond.

The role involves developing mathematical models, evaluating radiological risks, supporting government agencies, and disseminating findings through journals and conferences. Candidates should have a PhD and strong programming skills in Fortran, C/C++, Python, or MATLAB.

Qualifications

• PhD in physics, geophysics, atmospheric sciences, oceanography, or related field.
• Proficiency in Fortran, C/C++, Python or MATLAB; Linux and HPC experience.
• Strong analytical and scientific writing skills.
• Experience with numerical modelling preferred.

Responsibilities

• Develop comprehensive dispersion models for radionuclide transport.
• Assess radiological impacts on humans and environment.
• Provide scientific and technical support to Singapore government agencies.
• Publish findings in peer-reviewed journals and present at conferences.

Skills

Strong analytical abilities

Education

PhD in physics/geophysics/oceanography/atmospheric sciences or related field

Tools

Fortran

C/C++

Python

MATLAB

Linux

High-performance computing

Job description

Company description:

The National University of Singapore is the national research university of Singapore. Founded in 1905 as the Straits Settlements and the Federated Malay States Government Medical School, NUS is the oldest higher education institution in Singapore

Job description:

The Singapore Nuclear Research and Safety Institute (SNRSI), a university-level research institute at NUS established in 2014, is committed to excellence in nuclear safety research. Within SNRSI, the Radionuclide Dispersion team specialises in modelling the migration and impact assessment of radioactive pollutants through various pathways in Singapore and the surrounding region. The team is dedicated to advancing the academic community while tackling real-world challenges faced by government agencies and stakeholders.

With several positions open for Research Fellow, we welcome motivated candidates to join us - an excellent opportunity to collaborate with colleagues from world-leading universities and develop essential skills in the exciting world of modelling and cutting-edge technologies, including data analysis and visualisation, machine learning, computational methods, and more.

If offered the position, you will participate in the following research activities relating to the comprehensive dispersion modelling of radionuclides:

• Development of comprehensive dispersion models

Develop mathematical models to simulate the transport, migration, and redistribution of radionuclides across multiple environmental media, including the atmosphere, aquatic systems, terrestrial environments, and food chains. The modelling will cover Singapore and, where necessary, extend to the wider regional or global scale.

• Radiological impact assessment

Assess the radiological impacts on humans and the environment by evaluating the distribution and the fate of radionuclides following their releases into the environment through various pathways.

• Technical support

Provide scientific and technical support to relevant Singapore government agencies and other stakeholders in areas of nuclear safety, emergency preparedness, and environmental impact assessment.

• Research dissemination

Publish research findings in peer-reviewed journals and present at scientific conferences.

Qualifications

• A PhD degree in a relevant discipline, such as physics, geophysics, atmospheric sciences, oceanography, or related field.
• Proficiency in scientific programming languages such as Fortran, C/C++, Python, or MATLAB, with familiarity in the Linux operating system and high-performance computing environments.
• Strong analytical and problem-solving abilities, with good scientific writing skills.
• Experience with numerical modelling is strongly preferred.