Job Description

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 solve most exciting problems in the future 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 cultivate next-gen AI scientists. If you are passionate about advancing AI alignment in multicultural societies and enabling scalable, efficient LLM adaptation, we encourage you to apply.

Successful candidates will be responsible, but not limited to:

Develop alignment frameworks to create social value-sensitive representations for sub-groups and to enable inference-time output refinement aligned with sub-group values.

Design and implement parameter-efficient fine-tuning techniques within the LLM alignment context.

Develop models for trajectory prediction and route reconstruction of ocean-going vessels using AIS data, satellite imagery, and contextual information.

Build machine learning pipelines to optimize port operations, including vessel arrival forecasting, berthing planning, and congestion mitigation.

Collaborate with interdisciplinary teams to integrate domain knowledge from linguistics, social sciences, and maritime technology.

JOB REQUIREMENT:

PhD in Computer Science, Artificial Intelligence, Machine Learning, or related fields.

Strong background in AI, reinforcement learning, and deep learning, with expertise in areas such as generative models, agent learning, or spatio-temporal modeling.

Solid foundation in mathematics, capable of independent theoretical proofs, algorithm design, and integration with machine learning, optimization, and statistical modeling.

Experience with Maritime Informatics and LLM alignment techniques, such as Reinforcement Learning with Human Feedback (RLHF).

Proficiency in developing efficient AI models, including parameter-efficient fine-tuning or inference-time adaptation methods.

Strong publication records in prestigious conferences and journals, such as TPAMI, NeurIPS, ICML, ICLR, AAAI, IJCAI, JMLR, TKDE, AI.

Ability to work independently and collaboratively within interdisciplinary research teams.

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 :

1 Year

Work Location :

Fusionopolis