Research Scientist

3 days ago

Singapore A*STAR - Agency for Science, Technology and Research Full time

Overview
A*STAR Centre for Frontier AI Research (A*STAR CFAR) is seeking an innovative Research Scientist to contribute to the development of cutting-edge AI technologies in maritime applications, value alignment, and resource-efficient large language model (LLM) adaptation. This role focuses on advancing alignment techniques for large language models (LLMs), enabling fine-grained, sub-group specific alignment deployable on personal devices. The candidate will also work on developing and applying machine learning techniques to solve practical challenges in vessel route prediction, port operations optimization, and global trade flow analysis.
Responsibilities
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.
Qualifications
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.
Job details
Seniority level: Associate
Employment type: Full-time
Job function: Other
Industries: Research Services
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