The School of Computer Science and Engineering (SCSE) invites applications for the position of Research Fellow.
Key Responsibilities:
- Conduct independent research in the filed of knowledge continual transfer and multimodal compositionality based on LLM, including multi-modal compositionality, compositional reasoning with commonsense knowledge, and LLM-based compositionality understanding and reasoning.
- Develop algorithms for continuous knowledge transfer and compositional reasoning to facilitate efficient learning of new tasks.
- Collaborate with a research group member to design and implement algorithms aimed at enhancing machine intelligence through knowledge transfer and compositionality.
- Apply developed algorithms to real-world applications with sparse data
- Stay abreast of the latest research in machine learning, artificial intelligence, and related fields to incorporate new techniques into algorithm development efforts.
- Help supervise PhD, master and undergraduate students.
Job Requirements:
- PhD degree in computer science, software engineering, mathematics, or a related field.
- Strong background in multimodal learning, natural language processing, domain adaptation, and knowledge graph, particularly in algorithm development and implementation.
- Familiarity with knowledge transfer techniques and compositionality in machine learning is highly desirable.
- Good publication track record is a must.
- Proficiency in programming languages such as Python, and experience with machine learning libraries (e.g., TensorFlow, PyTorch).
- Excellent problem-solving skills and ability to work independently as well as part of a team.
- Passion for applying machine learning algorithms to real-world problems, particularly in domains with limited data such as drug discovery.
- Effective communication skills and ability to convey complex technical concepts to both technical and non-technical stakeholders.
We regret that only shortlisted candidates will be notified.
