Statistics Forms

Company NameTsinghua University

Position TitleDigital Twin Research Post-Doctoral Fellow

Company Information

We invite applications for a Post-Doctoral Research Fellow position in the broad area of AI-enabled digital twins for complex engineering and scientific systems. The successful candidate will develop data-driven and model-informed digital twin methodologies for representing, estimating, predicting, and optimizing the behavior of complex systems.

The position is intentionally interdisciplinary and methodologically oriented. Potential application domains include, but are not limited to, semiconductor testing and manufacturing systems, intelligent equipment, advanced manufacturing, cyber-physical systems, healthcare and biomedical systems, reliability engineering, and scientific discovery platforms.

The project aims to build generalizable digital twin frameworks that integrate data, domain knowledge, statistical learning, machine learning, simulation, visualization, and decision-support mechanisms. The postdoctoral fellow will have opportunities to work on both methodological innovation and prototype system development, with applications tailored to ongoing collaborative projects.

Duties and Responsibilities

The postdoctoral fellow will participate in one or more of the following research directions:

1. Digital Twin Modeling for Complex Systems
Develop general digital twin frameworks for representing system states, behaviors, dynamics, and uncertainties across complex engineering or scientific systems.

2. Data-Driven and Model-Informed Learning
Integrate statistical learning, machine learning, time-series modeling, simulation, and domain knowledge to estimate system states, predict trends, and quantify risks.

3. Cross-System Generalization and Transfer Learning
Study how digital twin models can be aligned, transferred, and generalized across different systems, devices, sites, populations, or operating conditions.

4. Intelligent Monitoring, Prediction, and Decision Support
Develop methods for anomaly detection, health assessment, risk prediction, adaptive monitoring, and AI-assisted decision-making.

5. Prototype Development and Visualization
Design and implement digital twin prototype systems that support state visualization, comparative analysis, model interpretation, and integration with future AI agents or decision-support modules.

Potential Application Domains
• Semiconductor testing, ATE, burn-in systems, and silicon lifecycle management;
• Intelligent manufacturing and industrial equipment monitoring;
• Cyber-physical systems and engineering reliability;
• Healthcare, biomedical, and personalized digital twins;
• Scientific platforms requiring simulation, prediction, and adaptive experimentation;
• AI-enabled research infrastructure and decision-support systems.

Position Qualifications

Applicants should have a Ph.D. degree, or expect to receive one soon, in one of the following or related fields:
• Electrical Engineering, Computer Engineering, Computer Science;
• Statistics, Data Science, Machine Learning, Applied Mathematics;
• Industrial Engineering, Mechanical Engineering, Biomedical Engineering;
• Semiconductor Manufacturing, Intelligent Manufacturing, Reliability Engineering;
• Systems Engineering, Cyber-Physical Systems, or related areas.

Strong candidates will have expertise in one or more of the following areas:
• Machine learning, statistical modeling, time-series analysis, or representation learning;
• Digital twin systems, simulation-based modeling, or cyber-physical systems;
• Anomaly detection, predictive maintenance, uncertainty quantification, or decision support;
• Data engineering, log analysis, multimodal data integration, or large-scale industrial/scientific data;
• Software prototyping, visualization, dashboard development, or AI-agent integration.

Experience in semiconductor testing, manufacturing, biomedical systems, or industrial equipment data is welcome but not required.

Application Instructions

Interested applicants should submit:
1. Curriculum vitae;
2. Cover letter describing research interests and relevant experience;
3. Representative publications or writing samples;
4. Research statement, preferably describing ideas related to digital twins, AI-enabled modeling, or complex system intelligence;
5. Contact information for 2-3 references.
Please send application materials to: sufei@tsinghua.edu.cn
Email subject line: Application for Digital Twin Research Post-Doc Position – [Your Name]