The School of Artificial Intelligence (SAI) at The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) is pleased to announce that applications for its 2026 full-time MPhil-PhD programme in Artificial Intelligence are now open to talented students worldwide.

We accept applications on a rolling basis, so we encourage you to apply early—admission is closed once all spots are filled.

Supervising Model

I. Doctoral Programs

Doctoral training follows a principal supervisor system. Supervision may be provided by a single principal supervisor or by a supervisory team consisting of a principal supervisor and one or more co-supervisors. The principal supervisor must be a faculty member of the School of Artificial Intelligence. For details on faculty members, please visit: https://sai.cuhk.edu.cn/teacher-search.

II. Master's  Programs

Research-oriented master's programs adopt an integrated training model that combines individual supervision with team-based mentorship. The principal supervisor system ensures personalized and focused academic guidance, while research groups provide a collaborative research environment, offering interdisciplinary platforms and strong project support.

Within this framework, students benefit from a multi-dimensional mentoring structure comprising an academic supervisor, an engineering supervisor (where applicable), and a research team, designed to strengthen innovation capacity and practical research skills. Research groups may include experts from outside the School. At the time of enrollment, master’s students may either identify a principal supervisor or defer the selection and determine their supervisor progressively through a mutual selection process during their studies.

A list of available research groups is provided below, categorized to address key AI challenges and opportunities. (Please click on the name of each research group for more details)

1. AI Core

(1) Trustworthy AI: This research group is dedicated to building safe, reliable, and ethical next-generation AI systems. Our research focuses on the adversarial defense, privacy protection, value alignment, and algorithmic fairness of large models and intelligent agents, aiming to drive the adoption of Trustworthy AI as a practical and deployable technical standard.

(2) Intelligent Optimization & Decision Analytics: This research group is dedicated to the development of next-generation intelligent decision-making algorithms. This is achieved by combining learning-based optimization and large language models with foundational approaches like integer programming and non-convex optimization. The application of these algorithms extends to practical domains such as industrial scheduling, revenue management, and large language model reasoning, with the aim of enabling high-quality, large-scale decision-making.

(3) Knowledge Graph & Large Language Model: This research group is dedicated to integrating Knowledge Graphs with Large Language Models to transform fragmented multimodal data from the fields of industrial and software engineering into computable, structured knowledge. Our goal is to construct the "knowledge brain" for next-generation intelligent systems, thereby empowering the leapfrog development of industrial intelligence and software engineering.

(4) AI Data Governance: This research group focuses on data and compliance governance throughout the entire AI lifecycle. We are committed to establishing a new paradigm of tripartite synergy among "model, data, and governance," aiming to advance AI's development by unlocking its full potential while ensuring it remains trustworthy and controllable, thereby serving the national digital strategy and the development of an intelligent governance system.

(5) Advanced AI Systems and Infrastructure: This research group focuses on developing end-to-end AI systems by integrating novel deep learning algorithms with scalable training, deployment, and performance optimization workflows. We emphasize the full AI system lifecycle, from large-scale data collection and model training to evaluation, tuning, and efficient inference on distributed infrastructures. Our goal is to build robust, high-performing, and scalable AI systems capable of operating reliably in complex, real-world environments.

(6) Reinforcement Learning & Responsible AI: This research group advances Reinforcement Learning as a core paradigm for building adaptive, interactive AI systems, while integrating responsible AI principles to ensure alignment with human values, transparency in decision-making, and robustness in real-world deployment.

2. Embodied AI & Robotics

(1) Embodied AI for Multi-robot Collaboration: This research group is committed to advancing multi-robot systems through several key areas: developing cross-scale autonomous collaborative localization and communication; exploring collaborative environmental perception under spatiotemporal variance; and creating decision-making and planning frameworks that bridge loosely and tightly coupled approaches, all to push the boundaries of embodied intelligence in multi-robot collaboration.

(2) Intelligent Systems: This research group focuses on developing intelligent decision-making systems capable of autonomous learning in unknown and uncertain environments. Our research emphasizes explainable AI models in domains such as robotics, perception, and communication. We are committed to constructing efficient and analyzable novel design schemes for intelligent systems through observation and reasoning about physical systems.

(3) Geospatial Intelligence: This research group specializes in Geospatial Intelligence and Brain-inspired Navigation. We are dedicated to developing intelligent agents capable of perceiving and reasoning in complex environments, driving innovation in high-precision positioning and adaptive learning technologies.

(4) Embodied AI: From Principle to Practice: This research group investigates the fundamental principles required to achieve embodied AI and develops practical applications in areas such as navigation, manipulation, and human/multi robot collaboration. The goal is to advance embodied AI both theoretically and technically.

(5) Language and Interactive Intelligence: This research group focuses on the intersection of artificial intelligence, language technology, and interactive intelligence. AI is transforming spoken and natural language processing, enabling breakthroughs in speech recognition, machine translation, dialogue systems, text understanding, multilingual communication, and generative AI for text and speech. The revolution in Language and Interactive Intelligence is exemplified by the rapid integration of AI into applications that shape how humans interact, communicate, learn, and collaborate across languages and modalities.

(6) General-Purpose Embodied Control: This research group focuses on advancing AI algorithms and control theory for embodied intelligence robots. We integrate cutting-edge AI methods to develop intelligent robotic platforms stable, adaptive, safe, and generalizable interactions with the physical world.

3. AI for Sciences, Engineering and Medicine

(1) AI for Science | Intelligent Robotic Closed-Loop Experiments × New Energy Materials Discovery: This research group is dedicated to constructing a "closed-loop system": AI algorithms plan experiments → robotic platforms execute with precision → experimental data provides real-time feedback → models continuously update and self-optimize. Our goal is to explore complex systems with greater speed and precision, addressing major challenges such as the discovery of novel functional materials, the synthesis of drug candidates, and the analysis of complex biological pathways, ultimately providing actionable solutions for global issues including sustainable development, healthcare, and advanced technology.

(2) Artificial Intelligence of Things (AIoT): This research group is dedicated to the study of distributed machine learning and artificial intelligence technologies to drive the evolution and development of next-generation wireless networks.

(3) Geospatial Intelligence: This research group specializes in Geospatial Intelligence and Brain-inspired Navigation. We are dedicated to developing intelligent agents capable of perceiving and reasoning in complex environments, driving innovation in high-precision positioning and adaptive learning technologies.

(4) AI-powered Urban Transportation Systems: We are building the next generation of AI-powered urban transportation systems: modeling how people and vehicles move, predicting congestion before it happens, evaluating public transit networks for fairness and resilience, and simulating policy impacts through digital twins.

(5) AI-driven Biometrics: This research group focuses on leveraging artificial intelligence to analyze physiological signals, such as gait and voice, to develop biometric identification technologies. Our work aims to advance the clinical application of unobtrusive health monitoring and disease diagnosis.

4. AI for Social Sciences

(1) AI in Education Research Group—— Empowering Education with Intelligence: Innovative Teaching Research for Basic and Higher Education: This research group is an interdisciplinary platform established by the School of Artificial Intelligence and the Institute of Contemporary Education, focusing on the core challenges and innovative practices of empowering educational transformation through AI technology. We are dedicated to translating cutting-edge AI and robotics technologies into actionable and evaluable educational solutions, with a particular emphasis on the popularization of science and technology education, the cultivation of computational thinking, and the systematic reconstruction of teaching evaluation systems. Through rigorous design research and empirical exploration, we aim to foster the establishment of new educational paradigms adapted to the era of intelligence.

(2) Econometric Learning: This research group aims to address the key challenges, such as endogeneity bias, that arise when empowering causal inference with machine learning. We are committed to ensuring the scientific rigor of econometric models while leveraging data-driven advantages, with the goal of providing new tools for economic research.

(3) AI-driven Social-economic Studies: This research group aims to leverage artificial intelligence to simulate socioeconomic systems, bridging the gap between technological advancement and social application. We provide actionable insights for the platform economy and public policy, with the ultimate goal of achieving more equitable AI-enhanced governance.

(4) AI Data Governance: This research group focuses on data and compliance governance throughout the entire AI lifecycle. We are committed to establishing a new paradigm of tripartite synergy among "model, data, and governance," aiming to advance AI's development by unlocking its full potential while ensuring it remains trustworthy and controllable, thereby serving the national digital strategy and the development of an intelligent governance system.

5. AI for Medicine and Health

(1) AI-driven Precision Pathogenomics and Personalized Therapeutics: This research group focuses on the cutting-edge intersection of artificial intelligence and life sciences. We are dedicated to building precise and interpretable clinical intelligent analysis systems by fusing multimodal medical data with bioinformatics. Our research directions span intelligent diagnosis, pathogen detection, and public health emergency response, with the ultimate goal of driving the clinical translation and real-world application of AI technologies in the fields of precision medicine and healthcare.

(2) Embodied Intelligence for Next-generation Medicine: This research group focuses on integrating cutting-edge technology, including learning-based algorithms, novel surgical robot platform, new imaging modalities and processing methods, and miniature medical robotics, to achieve next-generation medical and surgical operations with high precision and effectiveness.

(3) AI in Clinical Medicine: This research group focuses on the interdisciplinary frontier of AI and clinical medicine. The goal is to develop reliable AI models to address critical challenges in clinical medicine and to translating the developed systems into clinical practice.

6. AI for Social Sciences

(1) Industry-University Innovation Network: Bridging Academic Excellence with Real-World Impact: Industry-University Innovation Network is a collaborative platform under the School of Artificial Intelligence (SAI) at CUHK-Shenzhen dedicated to bridging academic excellence with real-world industrial needs. The group connects faculty, students, staff, and industry partners to co-create impactful AI solutions through joint projects, internships, seminars, and technology transfer and collaboration initiatives.

Anchored in GBA’s innovation ecosystem, the network focuses on transforming cutting-edge research into deployable applications across sectors. By fostering long-term partnerships and practice-oriented education, it aims to enhance students’ industry-readiness, support enterprises in their digital and AI-driven transformation, and strengthen CUHKSZ SAI as a leading AI institution..

Application Guidelines

When submitting the online application at https://pgapply.cuhk.edu.cn/, applicants may list up to three preferred supervisors and up to three research groups that match their interests. If you have previously contacted a supervisor or group, please indicate this in parentheses.

Example format for the "Intended Supervisor" field:
"1. Professor Name A; 2. Professor Name B (Contacted); 3. Professor Name C. Intended Research Group: 1. Research Group Name A; 2. Research Group Name B (Contacted); 3. Research Group Name C."

For detailed application instructions, please visit: https://sai.cuhk.edu.cn/node/35.

Contact Us

Phone: +86-755-23519400

Office Hours: 8:30 AM - 12:00 PM, 1:30 PM - 5:30 PM (Monday to Friday, GMT+8)

Email: pg_sai@cuhk.edu.cn

Office Address: 4th Floor, TA Building, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Avenue, Longgang District, Shenzhen, Guangdong Province, China.