Part-Time Research Assistant (Machine Learning and Computational Fluid Dynamics) Part-time

Research Assistant For Next-Generation Computational Tools

The School of Mechanical & Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE prides itself in its excellent research capabilities in areas including advanced manufacturing, aerospace, biomedical, energy, industrial engineering, maritime engineering, robotics, etc. The school is equipped with state-of-the-art research infrastructure, housing a comprehensive range of cluster laboratories, test bedding facilities, research centres/institutes and corporate laboratories. Cutting-edge research in MAE addresses the immediate needs of our industries and supports the nation's long-term development strategies. In the new era of industrial 4.0 and sustainable living, MAE is rigorous in developing new competencies to support the growth and competitiveness of our engineering sector in the global landscape. MAE has grown to be leader in Engineering Research, ranking amongst the top engineering schools in the world.

We are seeking to hire a part-time research assistant to develop of next-generation computational tools for simulating two-phase flows by integrating advanced Artificial Intelligence (AI) techniques with traditional computational fluid dynamics (CFD) methods. The role focuses on transitioning legacy CFD solvers to modern, Python-based AI frameworks (e.g., PyTorch, JAX), targeting high-efficiency, scalable simulations of water-air systems transport. The successful candidate will contribute to the creation of an AI-optimized platform capable of achieving up to 50× speedups in simulation performance, enabling real-time, energy-efficient modelling for complex multiscale flow phenomena relevant to coastal engineering. This position is central to advancing AI-augmented fluid dynamics and delivering an open, sustainable simulation tool for the research and engineering community.

Key Responsibilities:

• Development of AI-augmented CFD frameworks
• Simulation two-phase flows
• Validate the numerical model with experimental or existing numerical work
• Engage in interdisciplinary collaboration

Job Requirements:

• Bachelor degree or higher in Mechanical Engineering, Computational Science, Applied Mathematics, or a related field.
• Strong in critical thinking and problem-solving, with effective communication, teamwork, time management, and self-motivation skills
• Strong foundation in CFD with proficiency in Python and AI/ML techniques, and additional experience in parallel computing tools such as CUDA and MPI
• Experienced with CFD simulation tools (e.g., OpenFOAM, Ansys Fluent) is advantageous
• Strong track record of peer-reviewed publications in relevant fields.
• Capable of developing novel computational strategies beyond traditional CFD paradigms, with a strong enthusiasm for exploring and applying emerging AI techniques to engineering challenges.
• Capable of proposing and implementing novel computational strategies that go beyond traditional CFD paradigms,
• Enthusiastic about exploring new AI paradigms and applying them to engineering challenges.
• Be able to work at least 2 days each week

We regret to inform that only shortlisted candidates will be notified.

Hiring Institution: NTU