Job Description

We are seeking a highly motivated and experienced Senior Scientist to lead and contribute to cutting-edge research and development projects in coastal protection. The successful candidate will be responsible for advanced modelling of hydrodynamics, flow-structure and flow-vegetation interactions, and sediment transport processes through a combination of large-scale physical experiments and numerical simulation techniques. This work will play an important role in supporting sustainable, climate-resilient coastal development.

Key Responsibilities:

Lead and support large-scale physical modelling projects focused on coastal protection, encompassing hard infrastructure, nature-based solutions, and green-grey hybrid measures.

Conduct numerical simulations using open-source CFD software (e.g., OpenFOAM) and commercial modelling tools to simulate coastal processes and support the development of effective coastal protection strategies.

Participate in field studies and collaborate with multidisciplinary teams to monitor and evaluate the performance of coastal protection solutions from both engineering and ecological perspectives.

Support structural and geotechnical assessments to evaluate the stability of coastal protection systems.

Integrate artificial intelligence (AI) tools into modelling workflows to enhance predictive capabilities, solution optimization, and computational efficiency.

Manage projects effectively, ensuring alignment with timelines, budgets, and stakeholder expectations, including grantors and industry partners.

Prepare and deliver technical reports, scientific publications, and presentations to internal and external stakeholders.

Perform additional duties as assigned by supervisors to support team and organizational objectives.

Requirements:

PhD in Civil Engineering, Coastal Engineering, Ocean Engineering, or a related discipline.

Minimum 6 years of research or industry experience in coastal engineering or related fields.

Demonstrated expertise in the modelling of flow-structure interaction, flow-vegetation interaction and/or sediment transport processes; experience with physics-informed machine learning approaches is an advantage.

Proven ability to work independently and within collaborative teams.

Strong problem-solving skills and resourcefulness in addressing complex technical challenges.

Ability to manage tasks within defined timelines and budgets.

Excellent written and verbal communication skills, with experience in stakeholder engagement and technical documentation.

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.

Type of Employment :

Full-Time

Minimum Experience :

6 Years

Work Location :

NUS