Job Description
Singapore’s convective weather is highly complex and rapidly evolving, creating significant uncertainty in forecasting its frequency, severity, and spatial distribution. As a future-ready connectivity hub, this directly affects the efficiency, reliability, and safety of our aviation and maritime operations, leading to disruptions, increased costs, and heightened safety risks from severe weather such as heavy precipitation and lightning. To strengthen national capabilities, we are advancing AI-enabled localized tropical weather prediction under projects secured through the Aviation Meteorological Programme (AMP) and Weather Science Research Programme (WSRP).
We are seeking highly motivated Research Scientists and Engineers to advance our AI-enabled weather intelligence research. The role focuses on conducting cutting-edge, data-driven research on tropical convective hazard prediction using multi-modal datasets from weather observation systems and Numerical Weather Prediction (NWP) models. Successful candidate will work with a team of data scientists and engineers to develop, implement, and evaluate advanced machine learning and deep learning models for multi-source, multi-scale weather forecasting, leveraging state-of-the-art AI techniques to improve the accuracy and spatio-temporal resolution of hazardous weather predictions such as heavy precipitation, strong winds, and lightning. Beyond methodological innovation, you will also play a key role in translating research breakthroughs into operationally deployable solutions, working in a multi-disciplinary team comprising atmospheric scientists and operational stakeholders.
This work directly contributes to delivering actionable hazard intelligence to support real-world applications, including safer and more efficient air and ground operations in tropical environments. You will join a high-impact team addressing key challenges at the intersection of AI and weather science.
Job Requirements
- Ph.D in Computer Science, Computer Engineering, Data Science, or a related discipline with rich AI experience
- Strong programming skills, with proficiency in Python and PyTorch, and experience in optimizing deep neural networks for large-scale, multi-GPU training.
- Experience in data analysis, cleaning, and visualization, particularly for real-world sensor data (e.g., radar, satellite, lightning, and weather station data).
- Expertise in deep learning, especially in spatio-temporal modelling, generative AI, physics-informed learning, and uncertainty modelling.
- Experience in dense video prediction tasks such as video prediction or precipitation nowcasting is highly desirable.
- Knowledge of tropical weather systems or atmospheric science is a plus.
- Demonstrated ability to conduct independent, high-quality research and translate ideas into deployable prototypes.
- Strong communication and collaboration skills, with the ability to work effectively with domain experts and operational stakeholders.
- A strong sense of ownership, research integrity, and the ability to deliver high-quality work under tight timelines.
- A genuine passion for advancing AI technology and translating research outcomes into real-world operational impact.
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.
