The College of Computing & Data Science (CCDS) invites applications for the position of Research Fellow.
The Research Fellow will conduct research on cutting-edge large language models (LLMs) and the application on education. This role involves leading research efforts, developing innovative algorithms, managing large datasets, and ensuring the system's accuracy and fairness. The researcher will collaborate with interdisciplinary teams, publish findings, and integrate the scoring system into educational platforms.
Key Responsibilities:
- Conduct the research on LLMs and lead the application of LLMs in an educational system.
- Conduct literature reviews to stay updated on the latest advancements in natural language processing (NLP), AI, and automated essay scoring.
- Develop innovative algorithms and models to handle the specific characteristics of Chinese text, including syntax, semantics, and idiomatic expressions.
- Implement and fine-tune LLMs to accurately assess various aspects of essay quality, such as grammar, coherence, argument strength, and style.
- Data Collection and Annotation: Collaborate with educational institutions to collect large datasets of Chinese essays.
- Oversee the annotation process to ensure high-quality, consistent labeling of essay features relevant to scoring.
- Develop and maintain a robust database for managing essay datasets and annotations.
- Dissemination and Collaboration: Publish research findings in leading academic journals and present at conferences and workshops.
- Collaborate with interdisciplinary teams, to enhance the research and its applications.
Job Requirements:
- A PhD in AI, NLP, or a related field.
- Strong background in natural language processing and machine learning, with specific experience in working with large language models.
- Proficiency in Chinese language as required for research work.
- Strong programming skills, with proficiency in Python and relevant machine learning frameworks (e.g., TensorFlow, PyTorch).
- Excellent communication and teamwork skills, with the ability to collaborate effectively with interdisciplinary teams.
We regret that only shortlisted candidates will be notified.
