HKUST(GZ) DIGAI Lab Recruitment

Update: 2025-07-17

The Data Intelligence and Geometric AI Laboratory (DIGAI Lab) at HKUST(GZ) is actively seeking passionate students to join our cutting-edge AI research team. We offer opportunities for PhD studies and remote visiting research positions. Our research spans representation learning, LLMs, geometric learning, and information retrieval, with a focus on advancing geometric AI theory and its applications in network science and scientific discovery. We welcome students from diverse backgrounds and provide extensive research resources and international collaboration opportunities.

1. Main Research Directions

Direction 1: LLMs & Geometry

This research focuses on enhancing LLM token embeddings and reasoning through geometric constraints, guidance, and priors. We leverage the intrinsic geometric structure of data and representation spaces to improve learning and reasoning processes.

Core Research Areas Include:

• LLM Embedding: We investigate the geometric properties of internal representation spaces in Transformers and other large models, exploring topological structures, manifold features, and geometric invariances in high-dimensional embedding spaces

• Multi-modal Alignment: We design unified representation frameworks using geometric constraints to align different data modalities (text, images, audio), achieving effective geometric alignment of multi-source heterogeneous data

• Reasoning & PEFT: We develop novel reinforcement learning algorithms and fine-tuning strategies based on geometric priors to optimize model parameters and improve reasoning accuracy and training efficiency

We welcome students interested in foundation models, representation learning, non-Euclidean spaces, hyperbolic geometry, or differential geometry and machine learning. Our goal is to achieve breakthrough progress in foundation models through interdisciplinary collaboration.

Direction 2: Personalization and Recommendation Systems

This research integrates knowledge graphs, personal data, and geometric modeling to build next-generation recommendation systems and personalized analysis frameworks. We address key challenges in personalized LLM applications, including hallucination problems, recommendation accuracy, social network dynamics, and group behavior prediction.

Core Research Areas Include:

• Retrieval-Augmented Generation (RAG, GraphRAG): We build personalized large models using knowledge graphs and localized data, enhancing performance through RAG and Graph RAG technologies. By combining structured knowledge with unstructured text, we achieve precise information retrieval and generation while reducing hallucination problems

• Recommendation Systems: We develop next-generation recommendation algorithms using deep learning and geometric constraints, integrating user behavior, content features, and social relationships. Our work addresses key challenges including cold start problems, data sparsity, and recommendation diversity

• Network Science & Graph Learning: We develop efficient algorithms and frameworks for large-scale complex network modeling, representation learning, and optimization. Our focus includes community detection, information propagation, influence analysis, and link prediction using graph neural networks and geometric deep learning

We welcome students interested in recommendation systems, social network analysis, knowledge graphs, graph neural networks, complex network theory, information retrieval, and related data mining and machine learning technologies.

Direction 3: AI4SCI: AI for Scientific Research

This research integrates cutting-edge AI technologies with scientific research, driving transformative progress in bioinformatics, biomedicine, and materials science through foundation models and advanced algorithms. We aim to build intelligent scientific research paradigms that accelerate scientific discovery and address major challenges in human health and sustainable development.

Core Research Areas Include:

• Biomedical Data Intelligence: We use LLMs and multi-modal models to process massive heterogeneous biomedical data, including gene sequence analysis, protein structure prediction, drug discovery, and clinical data mining, achieving intelligent transformation from data to knowledge

• Geometric ML in Life Sciences: We combine geometric deep learning with bioinformatics to develop geometric constraint models for molecular structures, protein folding, and drug-target interactions, improving biomedical research precision and efficiency

We welcome researchers interested in computational biology, bioinformatics, medical informatics, drug design, and related interdisciplinary fields to join our team and contribute to AI4SCI innovation.

2. University Introduction

HKUST(GZ) is the first legally independent mainland-Hong Kong cooperative educational institution established under the Greater Bay Area development framework. Officially established in June 2022 with approval from China’s Ministry of Education, HKUST(GZ) focuses on interdisciplinary innovation and exploring new talent cultivation models. We aim to become a paradigm for integrated educational development and an internationally renowned university that cultivates innovative talents for the future.

HKUST(GZ) awards master’s and doctoral degrees from The Hong Kong University of Science and Technology. As of September 2024, we have over 300 academic staff, including 240+ tenured faculty. All faculty hold doctoral degrees, 98% have international experience, nearly 20% are national-level talent program recipients, nearly 50% are provincial/ministerial-level talent program selectees, and 15% are ranked among the global top 2% scientists.

Since establishment, HKUST(GZ) has been approved for 3 Guangdong Provincial Key Laboratories and 11 Guangzhou Municipal Key Laboratories. We have secured over 300 government-funded research projects, including 66 national-level projects and participation in 18 national key and major projects.

The university has signed cooperation agreements with over 90 leading enterprises and research institutions, including Alibaba Cloud, GTA Semiconductor, and Shenzhen Bay Laboratory. We have established joint laboratories with nearly 10 industry leaders, supported over 100 entrepreneurship incubation projects, and registered 40+ enterprises. The HKUST(GZ) Innovation Zone is under construction. We have established a 1 billion yuan technology transfer fund with Guangzhou Industrial Investment Group, with total fund partnerships reaching 2.4 billion yuan.

3. Team Introduction

DIGAI Lab is led by Dr. Menglin Yang (https://yangmenglinsite.github.io/), who holds a Ph.D. from The Chinese University of Hong Kong and conducted postdoctoral research at Yale University. He currently serves as an assistant professor in the AI Thrust at HKUST(GZ). Our team has extensive experience in machine learning, geometric AI, and scientific computing, maintaining close collaborations with top universities and research institutions worldwide. We are actively recruiting PhD students and research assistants to participate in cutting-edge research projects. Successfully admitted PhD students receive full scholarships. Our group is a vibrant academic family that emphasizes integrity and fairness, providing excellent office environments and computational support. Joining our team offers:

• Publishing Excellence: Opportunities to publish in top-tier ML, data mining, and AI journals and conferences, with support for international conference participation
• Mentoring & Academic Environment: Weekly one-on-one meetings with supervisor, harmonious group atmosphere, regular paper reading sessions, and systematic learning in ML, optimization, and statistics. We encourage student collaboration and respect individual research interests while providing domain guidance
• Research Skills Development: For students pursuing research careers, we provide systematic training in research methodology and thinking to enable independent project leadership and lab management
• Collaboration & Exchange: Extensive opportunities including long-term visits to HKUST Clear Water Bay campus, exchanges with world-class universities, and industry internships at leading companies like Tencent, Huawei, and Peng Cheng Laboratory

4. Recruitment Information

We welcome applicants with:

• Strong interest in large models, recommendation systems, network science, AI4SCI, and related fields
• Undergraduate/graduate backgrounds in computer science, mathematics, physics, bioinformatics, network science, etc.
• Strong English communication skills (IELTS 6.5 or TOEFL 80 required for admission)
• Solid programming skills in relevant languages
• We also recruit research assistants (RAs) and interns with competitive compensation based on experience and project involvement. Remote interns receive computational resources and comprehensive guidance, with opportunities to publish high-level papers. Outstanding performers receive priority consideration for doctoral admission.

PhD students can start in February or August 2026. Program duration is 3 years (with relevant research master’s degree) or 4 years (without). Tuition is 40,000 RMB/year, with full scholarships provided to all admitted students (~15,000 RMB/month).

📝 Master’s Student Application: Master’s applicants can apply directly through the university website without prior supervisor contact. If already admitted to a master’s program and interested in joining our lab, please contact us directly via email.

HKUST(GZ) master’s and undergraduate students are welcome to contact the supervisor directly.

5. How to Apply

Please send the following materials to menglinyang[at]hkust-gz.edu.cn or digailab[at]outlook.com

• Resume
• Undergraduate and graduate transcripts
• Professional ranking certificates (if available)
• Recommendation letters (if available)
• Representative papers or projects (if available)
• Research proposal (if available)

Email subject format: Position Applied + Your Name + Degree + Graduation University + Major

We will conduct preliminary screening and arrange interviews upon receipt of materials.

Official application portal: https://fytgs.hkust-gz.edu.cn/