EDB-IPP Project: Personalization and Long-Context Modeling in Large Language Models

Job description

Job Description: Rakuten Asia, in partnership with the Economic Development Board ( EDB ) through the Industrial Postgraduate Programme ( IPP ), is seeking new PhD students. We are looking for individuals with a robust understanding of deep learning, machine learning, and natural language processing to contribute to our innovative research projects. Essential requirements include proven hands-on expertise and strong engineering skillsets, specifically in the development and training of PyTorch models. IPP Programme Benefits Candidates successfully selected for this programme will receive full sponsorship for their postgraduate studies and will be hired by Rakuten Asia upon successful completion. Collaboration Model The collaboration will include joint supervision of PhD students, shared infrastructure access for large-scale experiments, and regular research exchange. Outputs will include publications, open-source prototypes, and scalable frameworks for personalized LLM deployment. Project Outline Project Title: Personalization and Long-Context Modeling in Large Language Models Introduction We are entering a phase in the development of Large Language Models (LLMs), where personalization, long-context understanding, and continual interaction with users are becoming critical differentiators. This research initiative seeks to push the boundaries of user-adaptive, memory-augmented, and privacy-aware LLMs that can reason over long-term history while preserving efficiency and alignment. Our team brings experience in training and evaluating cutting-edge LLMs, and we invite academic collaborators to join us in shaping the next generation of user-centric AI systems. Objectives This collaboration aims to: Advance foundational techniques for personalizing LLMs over long, evolving contexts. Develop scalable methods for encoding and decoding extended user interaction history. Benchmark and prototype systems that integrate memory, retrieval, and adaptation in real-world applications. Train and support PhD-level talent through joint supervision and research internships. Proposed Research Areas We propose collaboration across the following topics, with openness to refining based on shared interests: Long-Context Representation and Compression Explore architectures (e.g. retrieval-augmented, segment-aware transformers, state-space models) that can efficiently handle user histories spanning millions of tokens. Personalization without Fine-Tuning Develop modular personalization techniques using adapters and user embeddings. Emphasize continual learning methods that avoid full-model retraining. Alignment and Safety for Personalized Models Develop evaluation protocols and mitigation strategies to ensure personalized behavior remains aligned with safety constraints and social norms under extensive user adaptation. Efficient Infrastructure for Persistent Context Design systems that support long-term memory and personalization with low-latency access to evolving user state. Rakuten is an equal opportunities employer and welcomes applications regardless of sex, marital status, ethnic origin, sexual orientation, religious belief or age.