Artificial Intelligence Research Center

*Full-Time Research Fellow Invitation* Assistant/ Associate/ Full Research Fellows in Artificial Intelligence Research Center, Chang Gung University Artificial Intelligence Research Center (AIC) at Chang Gung University (CGU) currently invites applications for full-time Assistant, Associate, and Full Research Fellows. There are 2 positions to be filled. Research Fellow Positions: We are looking for candidates with research areas in machine learning, computer vision, natural language processing, smart medicine, smart manufacturing, robotics, metaverse and human-computer interaction. Candidates should have a Ph.D. degree in Artificial Intelligence, Computer Science, Data Science, Engineering, Biomedical or related areas. Senior candidates must demonstrate significant research achievement, and have an international reputation such as strong publication records, patents, industrial experience, or other academic achievements. CGU AIC is a research-oriented institute and teaching duties are not required. Application Deadline: (1)   There is no application deadline. However, applications received by Jun 15, 2026 will receive the fullest consideration. (2)   Application materials will be reviewed on a first-come, first-served basis. (3)   Interview date/time will be set up promptly if an application has received a favorable opinion by the committee. Online interviews can be arranged. Starting Date:  August 1, 2026 or February 1, 2027  CGU Artificial Intelligence Research Center offers an internationally competitive compensation package commensurate with the candidate’s professional experience. Successful candidates will be appointed at the level of Assistant, Associate or Full Research Fellow according to qualifications. Application Requirements: (1)   Cover letter (one-page). (2)   CV with higher academic degree details (undergraduate to Ph.D.), professional experiences, research statement, publication list, and a list of three professional references. (3)   Chang Gung University is an equal-opportunity employer following the international standard for diversity and inclusion. Applicants from all regions and nationalities are welcome. (4)   Please submit your application in PDF files or in a cloud storage link to Ms. Sue Liu (sueliu@cgu.edu.tw). About AIC @ CGU Artificial Intelligence Research Center (AIC), established with the support of the Formosa Plastics Group (one of the largest private enterprises in Taiwan), is a research center in the Chang Gung University (CGU). In addition to the Formosa Plastics Group companies, AIC works closely with the Chang Gung Medical Foundation (the largest healthcare system in Taiwan) on advanced medical research projects, as well as other research centers in Chang Gung University. AIC also has strong connections with many world-leading high-tech companies in Taiwan. AIC researchers work closely with the faculty in the College of Intelligent Computing (CoIC), many with joint appointments from both units. CoIC is the first academic college in Taiwan devoted to the research and education of the AI technology and application. CoIC has many highly-reputed research faculty with a strong international and/or industrial working experience. AIC researchers and CoIC professors conduct joint research projects using AIC’s server hardware, IoT equipment and other research facilities. The center/college joint research eco-system further provides the support for CGU university-wide and international AI research activities. Chang Gung University (CGU), a student centric and research-directed university with entrepreneurial spirits, was established by the founding Wang family of the Formosa Plastics Group. CGU is highly ranked globally in the QS World Universities Rankings 2023, and in the top 5 among the 151 universities in Taiwan. CGU has 4 colleges: College of Medicine, College of Engineering, College of Management, and College of Intelligent Computing. CGU offers a student-faculty ratio of 12:1, with ~7,000 students, ~600 full-time faculty and ~665 adjunct faculty members. The University strives for excellence in research, teaching, and innovative technology commercialization. Under strong support from the Formosa Plastics Group and the Chang Gung Medical Foundation (the largest private-owned healthcare system in Taiwan), CGU provides advanced yet practical training on industrial technology and medical sciences. CGU is located in the suburban area of the Taipei city, about 30 minutes to the city center by Metro (Mass Rapid Transit). For details about Chang Gung University, please visit  https://www.cgu.edu.tw/en Learn more about CGU: https://www.cgu.edu.tw/en AIC: https://www.cgu.edu.tw/aic-en

Topic: Transforming Assistive Oral Communication Technologies through Artificial Intelligence   Speaker: Prof. Yu Tsao, Research Fellow (Professor) and the Deputy Director at the Research Center for Information Technology Innovation, Academia Sinica Time : April 21, 2026 (Tuesday), 2:00 – 4:00 p.m. Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://reurl.cc/grQlk4 or scan QR code on poster   About the Speaker:  Yu Tsao (Senior Member, IEEE) received the B.S. and M.S. degrees in Electrical Engineering from National Taiwan University, Taipei, Taiwan, in 1999 and 2001, respectively, and the Ph.D. degree in Electrical and Computer Engineering from the Georgia Institute of Technology, At-lanta, GA, USA, in 2008. From 2009 to 2011, he was a Researcher at the National Institute of Information and Communications Technology (NICT), Tokyo, Japan, where he conducted re-search and product development in multilingual speech-to-speech translation systems, focusing on automatic speech recognition. He is currently a Research Fellow (Professor) and the Deputy Director at the Research Center for Information Technology Innovation, Academia Sinica, Tai-pei, Taiwan. He also holds a joint appointment as a Professor in the Department of Electrical Engineering at Chung Yuan Christian University, Taoyuan, Taiwan. His research interests in-clude assistive oral communication technologies, audio coding, and bio-signal processing. He serves as an Associate Editor for IEEE Transactions on Consumer Electronics and IEEE Signal Processing Letters. He received the Outstanding Research Award from Taiwan’s National Sci-ence and Technology Council (NSTC), the 2025 IEEE Chester W. Sall Memorial Award, and served as the corresponding author of a paper that won the 2021 IEEE Signal Processing Society Young Author Best Paper Award.   Abstract: This presentation provides an overview of AI-driven assistive oral communication technologies, encompassing both assistive speaking and assistive hearing domains. The first part focuses on assistive speaking technologies, highlighting intelligent diagnostic and enhancement frame-works for speech disorders. It introduces machine learning approaches for pathological speech classification, severity assessment, and targeted enhancement for conditions such as dysarthria, post-surgical speech impairment, and electrolaryngeal speech. The second part addresses assis-tive hearing, presenting recent advances in AI-based diagnostic and signal processing tech-niques for hearing disorders. Representative applications include automated detection of otitis media with effusion, as well as AI-driven speech generation and objective quality assessment methods for hearing aids and cochlear implants. By integrating speech enhancement, assess-ment, and generation within a unified AI framework, this presentation demonstrates the poten-tial of neural-based technologies to enhance communication effectiveness and accessibility, while underscoring the importance of interdisciplinary research in advancing next-generation, human-centered assistive systems.   Organizers: College of Intelligent Computing & Artificial Intelligence Research Center   ※ No registration needed.

Topic: Genomics, Big Data, and AI: New Approaches for Understanding Complex Diseases Speaker: Prof. AiRu Hsieh — Associate Professor, Department of Statistics, National Taipei University Time: March 25, 2026 (Tuesday), 12:10 – 14:00 Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://reurl.cc/lpdb2q or scan QR code on poster Registration: https://forms.office.com/r/iUTLaer75x or scan QR code on poster  About the Speaker:   Professor Ai-Ru Hsieh is a researcher in statistical genetics and biomedical data science. Her work focuses on integrating genome-wide association studies (GWAS), polygenic risk scores (PRS), Mendelian randomization (MR), and machine learning approaches to investigate the genetic architecture and causal mechanisms of complex diseases. By leveraging large-scale biobank resources and high-dimensional biomedical data, her research aims to develop interpretable models for disease risk prediction and biological discovery. Her work contributes to advancing precision medicine and improving our understanding of disease mechanisms at both clinical and population health levels. Abstract: Recent discoveries in human genetics are driving precision medicine forward. Large-scale genome-wide association studies (GWAS) have mapped thousands of variants related to complex diseases, offering insight into their biology. Polygenic risk scores (PRS) pull together these small genetic signals to flag people who might warrant earlier screening or closer monitoring. Mendelian Randomization (MR) provides a statistical framework for evaluating potential causal relationships between exposures and disease outcomes, offering support for targeted interventions. In addition, machine learning approaches provide powerful tools for feature selection and predictive modeling in high-dimensional biomedical datasets. Techniques such as LASSO, random forests, and gradient boosting models can identify informative genetic variants, biomarkers, and clinical variables from large-scale datasets. These approaches improve disease risk prediction, refine polygenic risk models, and help uncover hidden patterns in MR analysis. By integrating statistical genetics methods, machine learning algorithms, and large-scale biomedical data resources, we can build interpretable models for disease prediction and biological discovery. The findings from these approaches make us more understanding of disease mechanisms and guide more effective prevention and treatment efforts in both clinical and population health contexts. Organizers: Institute of Health Data Science ※ Registration needed.

Topic: Toward Human-Centered Physical AI Speaker: Prof. Yi-Ting Chen — Associate Professor, Department of Computer Science at National Yang Ming Chiao Tung University Time: March 24, 2026 (Tuesday), 2:00 – 4:00 p.m. Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://reurl.cc/EbkDzv or scan QR code on poster  About the Speaker:   Prof. Yi-Ting Chen is an associate professor in the Department of Computer Science at National Yang Ming Chiao Tung University. He received his Ph.D. degree in Electrical and Computer Engineering from Purdue University. He worked as a senior research scientist at Honda Research Institute USA. His research lies in Human-centered physical AI, intelligent driving systems, assistive robotics, and computer vision. Abstract: Physical AI has gained rapid momentum in recent years. Yet most systems remain optimized for benchmark performance rather than long-term human integration and real-world deployment. How can Physical AI truly empower people while operating safely, reliably, and at scale? In this talk, I introduce Human-Centered Physical AI, an interdisciplinary paradigm that integrates perception, learning, decision-making, embodiment, multi-agent interaction across physical and virtual environments, and deployment and evaluation. Beyond intelligent behaviors, it emphasizes human-in-the-loop evaluation, safety-aware learning, robustness under real-world uncertainty, participatory design, and system-level validation. By placing human needs, safety, and dignity at the core, this approach bridges capability and accountability to enhance autonomy, trust, and quality of life. I will present assistive feeding and meal preparation as representative use cases, highlighting how personalization, safety constraints, and uncertainty modeling shape the system design. Organizers: College of Intelligent Computing & Artificial Intelligence Research Center ※ No registration needed

Topic: The development of clinical AI cycle: from signal discovery to pragmatic trials and real-world deployment Speaker: Professor Chin Lin, Department of Medicine, National Defense Medical University Time: March 11, 2026 (Wednesday), 12:10 – 14:00 p.m. Venue: The Management Building, 11F, AI Lecture Hall or Livestreaming https://reurl.cc/GG72GG Registration: https://reurl.cc/R9AMeD or scan QR code on poster  About the Speaker:   Dr. Chin Lin is a data scientist whose work centers on establishing clinical evidence standards for medical artificial intelligence. His research integrates large-scale electronic health records, electrocardiography, medical imaging, and home-based sensing to develop AI systems that are not only accurate, but clinically actionable and outcome-improving. He has led or co-led more than ten pragmatic randomized controlled trials evaluating AI-enabled clinical workflows, with results published in Nature Medicine, NEJM AI, Radiology, and Nature Communications. His team developed an AI-ECG platform that has received TFDA approval and USFDA Breakthrough Device Designation, has been transferred to industry, and deployed across multiple hospitals and rural screening programs. A central theme of his work is treating AI not as a standalone model, but as a system-level intervention that links prediction, clinical action, and patient outcomes within real-world healthcare environments. Abstract: Recent advances in artificial intelligence (AI) have shifted medical AI research from model-centric performance reporting toward evidence-based clinical impact. In this talk, I introduce the concept of the clinical AI cycle, a comprehensive framework that describes how clinical AI can progress from signal discovery, through pragmatic clinical trials, to real-world deployment in healthcare systems. At the stage of signal discovery, I will illustrate how routinely collected data—such as electrocardiograms (ECGs), chest radiographs (CXRs), and electronic health records (EHRs)—can be leveraged using data-driven approaches and multimodal foundation models to identify latent disease risks. This paradigm enables a single examination to support opportunistic screening for multiple diseases, overcoming the traditional one-test-one-disease limitation. The second stage focuses on pragmatic trials, where AI models are evaluated not merely by predictive accuracy but by their ability to trigger pre-specified clinical actions and improve patient outcomes. By embedding AI alerts into real clinical workflows and evaluating them using randomized controlled and digital trial platforms, AI is positioned as an actionable decision engine rather than a passive prediction tool. Finally, in real-world deployment, I will demonstrate how clinical AI can be scaled to community screening, home healthcare, and wearable devices, and integrated with large-scale health databases. This deployment completes the clinical AI cycle, allowing continuous learning, post-deployment surveillance, and discovery of new clinical insights. Organizers: Institute of Health Data Science ※ Registration needed.