Scientist, Centre for Frontier AI Research, IAIC

A*STAR Research Entities (A*STAR); Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

CFAR, A*STAR is seeking an innovative researcher to join its AI4TranS Group and contribute to AI for environmental science. You will develop spatiotemporal, multimodal, and structure-aware AI methods for weather forecasting and hazard prediction, collaborating with researchers and engineers.

Applicants should have a PhD in a relevant field and a strong publication record, with experience in PyTorch and large-scale geospatial datasets.

Qualifications

• PhD in CS/AI/ML/Data science/atmospheric science with strong AI research.
• Strong publication record in AI conferences/journals.
• Experience with PyTorch and large-scale geospatial datasets preferred.
• Excellent programming, problem-solving and analytical skills.

Responsibilities

• Design and develop novel spatiotemporal and multimodal learning models for weather forecasting and hazard prediction.
• Curate, integrate, and analyse multimodal datasets (radar, satellite, lightning, NWP) and build evaluation pipelines.
• Collaborate with senior scientists to implement, benchmark, and translate research into project deliverables.
• Publish outcomes in top AI conferences and journals and contribute to reports.

Skills

Spatiotemporal learning

Multimodal learning

Computer vision

Sequence modelling

Graph learning

Weather/climate AI

PyTorch

Geospatial datasets

Education

PhD in CS/AI/ML/Data science/Atmospheric science

Tools

PyTorch

Job description

Job Description

CFAR, A*STAR is looking for an innovative researcher to join its AI for Transdisciplinary Science (AI4TranS) Group and contribute to the AI for environmental science programme. The candidate will focus on developing spatiotemporal, multimodal, and structure-aware AI methods for high-impact weather forecasting and hazard prediction, including lightning-related forecasting. Research topics may include spatiotemporal representation learning, multimodal data fusion, sequence and video prediction, graph-based learning, efficient attention, and uncertainty-aware forecasting using radar, satellite, numerical weather, lightning, and other geospatial data. The candidate will collaborate with research scientists, engineers, and domain partners to develop robust models and validate them on large-scale real-world datasets.

Successful candidates will be responsible, but not limited to:

1. Design and develop novel spatiotemporal and multimodal learning models for weather forecasting, hazard prediction, and related geospatial applications
2. Curate, integrate, and analyse multimodal datasets such as weather radar, satellite, lightning, numerical weather prediction, and other spatiotemporal data, and establish reliable evaluation pipelines
3. Work with senior scientists, and project collaborators to implement, benchmark, and improve research models, and translate research findings into project deliverables
4. Publish research outcomes in high-quality AI conferences and journals and contribute to project milestones and technical reports

ABOUT A*STAR AND CFAR

At A*STAR, we make it our mission to attract and develop a diversity of talent along the research, innovation, and enterprise value chain, with career paths developed for scientists, engineers, and entrepreneurs. In return, we commit to investing in the personal and professional growth of each of our officers.

CFAR has a team of exemplary AI researchers committed to solving some of the most exciting problems in frontier AI, including IEEE fellows, chairs, scholars, and advisors among some of the most highly cited researchers in the world. Our AI Centre provides a unique research environment for fundamental AI research and cultivates next-gen AI scientists. If you share our interests in solving challenging engineering problems, we welcome you to apply.

JOB REQUIREMENTS

1. PhD degree in computer science, artificial intelligence, machine learning, data science, applied mathematics, atmospheric science with strong AI experience, or other related fields
2. Solid research experience in one or more areas such as spatiotemporal learning, multi-modal learning, computer vision, sequence modelling, graph learning, deep learning, or weather/climate AI
3. Good publication record in reputable AI conferences and journals, such as NeurIPS, ICML, ICLR, AAAI, IJCAI, CVPR, KDD, TPAMI, AIJ, TNNLS, NMI, or related venues
4. Strong programming, problem-solving, and analytical skills; experience with PyTorch and large-scale scientific or geospatial datasets is preferred
5. Strong communication and presentation skills
6. Ability to work independently and collaborate effectively with researchers, engineers, and domain experts