Graduate Student & Postdoctoral Recruitment
1. Overview of the Research Group
Research Group of Hu Yang belongs to the Smart Civil Aviation Innovation Center at the Hangzhou International Innovation Institute of Beihang University. It is a young, dynamic, and highly innovative research team. The group currently includes 2 associate research fellows, 1 associate professor, 3 postdoctoral researchers, and more than 10 master’s and doctoral students, forming an efficient structure of “professor leadership – core support – collaborative teamwork.”
The group focuses on major national strategic needs—particularly the intelligent maintenance, digital-twin modeling, and resilience-reliability analysis of new-generation aerospace, maritime, and advanced manufacturing systems. It conducts both fundamental research and key technology development. Facilities include a high-performance computing cluster, industrial IoT data-acquisition platform, PHM (Prognostics & Health Management) simulation and verification system, digital-twin modeling environment (supporting SysML, Modelica, AnyLogic), and a complex-network analysis platform.
The group maintains close academic collaborations with Zhejiang University, City University of Hong Kong, Politecnico di Milano, and Université Paris-Saclay, and works jointly with Aviation Industry Corporation of China, Ltd., Aero Engine Corporation of China, China Aerospace Science and Technology Corporation, Commercial Aircraft Corporation of China, Ltd., Huawei 2012 Labs, Zhejiang Loong Airlines Co. Ltd, and Suparna Airlines on major research projects and joint laboratories—ensuring that our research achieves both theoretical depth and practical impact.
The host platform, the Smart Civil Aviation Innovation Center, is a key national collaborative hub for the digital-intelligent transformation of civil aviation, jointly established with ENAC (École Nationale de l’Aviation Civile, France). It covers the entire lifecycle of aircraft, engines, and onboard systems—spanning airworthiness, maintenance, air-traffic management, and airport operations—forming an integrated “sensing – modeling – decision – optimization” technology chain for safe, efficient, and resilient aviation operations.
Major Supporting Laboratories
Sino-Italian Joint Research Laboratory on Prognostics and Health Management for Intelligent Maintenance (PHM-IM):
A core platform for civil-aircraft PHM large-model research, integrating physical experimentation and virtual simulation. It includes a full-aircraft PHM simulator (Airbus A320/Boeing 737, 12 subsystems, 300+ parameters, millisecond-level fault injection), high-precision mechanical-fault test rigs (50 kN hydraulic load, 0.01 Nm torque sensors), and a dedicated computing cluster (128-core CPU, 1 TB RAM, 2 × NVIDIA A100 GPUs) supporting multimodal large-model training and inference.Sino-French Dassault Excellence Education Center:
Based on the Dassault 3DEXPERIENCE Platform R2024x, providing an MBSE (Model-Based Systems Engineering) collaborative environment for aircraft design, manufacturing, and maintenance. It integrates CATIA, SIMULIA, DELMIA, ENOVIA, and tools such as Abaqus, Simpack, Isight, and Magic MBSE, enabling high-fidelity digital-twin modeling and multidisciplinary optimization.Reliability Digital-Twin Laboratory & Fleet Operation Simulation Laboratory:
Equipped for reliability testing (–70 °C to +200 °C thermal chambers, semiconductor thermal-resistance tester, automated LabVIEW/MATLAB data acquisition) and fleet-level AnyLogic simulations with AR-based visualization and dynamic-scheduling optimization.
The platform jointly established the Sino-Italian Joint Research Laboratory on Prognostics and Health Management for Intelligent Maintenance (PHM-IM), led by Prof. Enrico Zio (Politecnico di Milano) and Prof. Rui Kang (Beihang), supporting joint training, seminars, and course development. Industrial partners provide over 150,000 A320 flight cycles, 2,000+ real maintenance records, and 1,000+ technical manuals, ensuring authentic data and application-driven validation.
With comprehensive facilities, abundant data resources, and strong international collaboration, the group offers graduate students a complete environment for high-level, engineering-oriented, and globally connected research.
2. Major Research Directions
The group’s core themes are PHM, digital-twin modeling, and system resilience for complex engineering systems in aerospace, energy, and intelligent manufacturing.
Our integrated framework of “multimodal intelligence – cyber-physical modeling – reliability assurance” currently includes three research thrusts:
Multimodal Large Models & Intelligent PHM Algorithms – Combining AI with physics-based modeling for fault prediction and health assessment of aircraft, rail, marine, and power-system equipment. Students gain experience with CNN-based image diagnosis, RNN/LSTM degradation modeling, and Transformer-based long-sequence learning, and may participate in national PHM system design and validation projects.
Digital-Twin Modeling & Intelligent Maintenance Optimization – Developing MBSE-based, multi-physics digital twins for system-level simulation and maintenance decision optimization; suited for students interested in modeling, simulation, and intelligent scheduling.
System Resilience & Belief-Reliability Analysis – Studying system evolution, uncertainty, and recovery mechanisms using complex-network and entropy-based methods, with applications to aerospace, power networks, and infrastructure systems.
Together these three directions form an integrated research architecture of Intelligence – Simulation – Assurance, providing theoretical and technical foundations for highly reliable, intelligent, and resilient engineering systems.
3. Applicant Requirements
Academic Background
Preferred majors: Computer Science, Artificial Intelligence, Automation, Control Science & Engineering, Mechanical Engineering, Aerospace Engineering, Systems Engineering, or Applied Mathematics.
Solid foundation in mathematics (linear algebra, probability & statistics, optimization, differential equations) with excellent grades.
Strong performance in core courses such as Machine Learning, Deep Learning, Data Structures & Algorithms, Signals & Systems, and Control Theory.
Technical Skills
Proficiency in Python, MATLAB, or C++; familiarity with PyTorch / TensorFlow.
Ability to implement neural-network models (CNN, RNN, LSTM, Transformer).
Competence in mathematical modeling and data analysis (NumPy, Pandas, Scikit-learn, Matplotlib).
Background knowledge in PHM, Digital Twin, Reinforcement Learning, or Complex Networks is an advantage.
Preferred Experience
Awards in national or international competitions (Math Modeling, RoboMaster, ACM ICPC, Kaggle, etc.).
Research or engineering project experience, patents, or publications.
National or university-level scholarships or academic honors.
Internships in aerospace, manufacturing, or AI enterprises.
Personal Qualities
Passion for research, self-motivation, and perseverance under pressure.
Teamwork, communication, and academic presentation skills.
Good English reading and writing; TOEFL/IELTS/GRE scores are advantageous.
Commitment to careers in aerospace, intelligent manufacturing, or AI-driven systems engineering.
4. Graduate Training & Expected Outcomes
The group adopts a “personalized + project-driven + outcome-oriented” training model. Supervisors design individualized study plans aligned with each student’s background and interests.
Expected achievements:
Publish at least one high-quality paper as first author (or co-first with the supervisor).
Apply for an invention patent or software copyright.
Lead or co-develop a prototype system or simulation platform.
Present research at international conferences (ICML, PHM Society, IEEE Reliability, ESREL, etc.).
Complete a ≥3-month internship in an enterprise or research institute with report and evaluation.
Earn academic awards or scholarships.
All students receive adequate research funding, HPC resources, and international exchange opportunities. Outstanding students are encouraged for joint PhD or overseas study and may be recommended to leading partners such as AVIC, CASC, Huawei, and COMAC for employment.
5. Career Development
Academic Track: Joint supervision and exchange opportunities with overseas laboratories. Excellent MSc/PhD students may receive recommendations for PhD or postdoctoral programs at universities such as Paris-Saclay, Politecnico di Milano, and City U Hong Kong (CSC-funded).
Industrial Track: Graduates join institutes such as AVIC, CASC, Huawei, DJI, Alibaba Cloud, or Zhejiang Loong Airlines as algorithm engineers, PHM specialists, system architects, or reliability analysts.
Innovation & Entrepreneurship: Students are encouraged to pursue technology transfer or start-ups; several alumni have successfully founded companies in intelligent maintenance and industrial AI.
6. Application & Contact
Prospective students passionate about intelligent systems, artificial intelligence, and systems engineering are warmly invited to apply.
Please email the following materials to yang_hu@buaa.edu.cn, using the subject line: “Master’s/PhD Application – Name – Undergraduate University – Major.”
For the scholarship, please refer to https://is.buaa.edu.cn/en/lxsq/yjs/ssyjs.htm, and https://is.buaa.edu.cn/en/jxj.htm for scholarship information.
Required documents:
CV (education, research/projects, awards, and skills)
Academic transcripts (Bachelor’s/Master’s)
Representative achievements (papers, patents, competition certificates, project reports, etc.)
Personal statement (~500 words: motivation, research interests, career goals)
(Optional) 1–2 recommendation letters
After an initial screening, shortlisted candidates will be invited for an interview assessing fundamentals, research potential, English communication, and project ideas.
7. Postdoctoral Positions
Please refer to https://h3i.buaa.edu.cn/info/1141/1391.htm for details.
Beihang’s Hangzhou Institute offers Category A Postdocs: ¥320,000/year and Category B Postdocs: ¥280,000/year (excluding government subsidies), plus a ¥150,000 research start-up fund.
Postdocs may also apply for Hangzhou and Yuhang District government incentives, with total funding up to ¥2.19 million, following the latest regional policies.
The Hu Yang Research Group is a vibrant, excellence-oriented team that values both academic achievement and personal growth. Here you will engage with frontier research topics, contribute to national flagship projects, and develop strong scientific and engineering skills. If you are curious, ambitious, and ready to challenge complex problems, join us to explore the future paradigm of intelligent system