System Engineering and Innovation Lab (SEIL LAB)
Research Field
Dr. Basanta Haobijam is an Assistant Professor in the Department of Electrical Engineering at National Taipei University (NTPU), Taiwan, and a certified NVIDIA Deep Learning Institute (DLI) instructor. He leads the System Engineering and Innovation Lab (SEIL), focusing on the convergence of Artificial Intelligence, Embedded Systems, and Real-Time Computing, with a strong emphasis on Edge AI and AIoT.
His research centers on designing efficient and reliable AI systems for resource-constrained edge devices, enabling real-time deployment in practical environments. His work spans computer vision, intelligent industrial systems, and healthcare analytics, including defect detection and quality inspection for smart manufacturing, as well as wearable sensing and medical applications.
Dr. Haobijam has extensive experience in applying AI to real-world systems through collaborations with industry and clinical partners. His research emphasizes robustness, scalability, and interpretability to ensure deployment-ready solutions.
He is a recipient of the Best Teacher Mentor Award at NTPU for two consecutive years (2024, 2025) and has been recognized in international AI competitions for developing impactful, application-driven solutions. He provides a hands-on research environment where students gain practical experience in developing and deploying intelligent systems, preparing them for both academic research and industry careers.
Lab Introduction: SEIL
The System Engineering and Innovation Lab (SEIL) focuses on developing intelligent systems at the intersection of Artificial Intelligence, Embedded Systems, and Real-Time Computing, with a strong emphasis on Edge AI and AIoT applications. The lab is dedicated to transforming advanced learning-based models into efficient and deployable solutions for real-world environments, particularly on resource-constrained edge devices. The lab focuses on bridging AI algorithms and real-world deployment through efficient, real-time intelligent systems.
Research Focus Areas
- Computer Vision & Intelligent Sensing
Real-time defect detection and quality inspection for smart manufacturing systems - Healthcare Analytics & Wearable Systems
Continuous monitoring and data-driven decision support using wearable sensing technologies - Edge AI & Deployment Systems
Development of efficient, real-time AI solutions optimized for resource-constrained edge platforms
The SEIL Experience
A major strength of the lab lies in practical implementation and deployment. We provide students with hands-on experience in developing, optimizing, and deploying intelligent systems using real sensors, embedded platforms, and edge devices (e.g., IoT systems and AI hardware platforms), with a strong focus on real-time performance, robustness, and scalability.
By bridging academic research with industry needs, students have opportunities to contribute to both high-impact research publications and real-world system development. Students develop both strong research capabilities and practical engineering skills aligned with industry demands.
Students with interests in Artificial Intelligence, Embedded Systems, IoT, or real-world system deployment are encouraged to apply.
- Edge Artificial Intelligence (Edge AI)
- AIoT and Smart Systems
- Computer Vision and Intelligent Sensing
- Medical Image Analysis and Healthcare AI
- Wearable Sensing and Human Motion Analysis
- Uncertainty-Aware and Explainable AI
- Embedded Systems and Real-Time AI Deployment
- Smart Manufacturing and Industrial AI (Defect Detection & Quality Inspection)
- NVIDIA Deep Learning Institute (DLI) Certified Instructor, NVIDIA (2026)
- NVIDIA University Ambassador, NVIDIA (2026)
- Best Teacher Mentor Award, National Taipei University (2025)
- Best Teacher Mentor Award, National Taipei University (2024)
- Senior EMI (English-Medium Instruction) Faculty Recognition, National Taipei University
- Award Winner, Global Hackathon (2025) – collaboration with Virginia State University and National Taipei University
- Award Winner, Global Hackathon (2024) – international collaboration (Virginia State University (VSU), National Taipei University (NTPU), Technical University of Moldova (UTM))
- Top Awards, AI Smart Application Innovation Competition, National Taipei University (2023–2025)
- Best Student Paper Awards,
IEEE International Conference on Networking, Sensing and Control (ICNSC 2016) and
IEEE International Conference on Complex Medical Engineering (ICME 2015)
Ph.D. in Electrical Engineering and Computer Science
National Taipei University of Technology, Taiwan
M.S. in Computer Applications (MCA)
Jamia Millia Islamia, India
B.S. in Computer Applications (BCA)
Guru Nanak Dev University, India
Job Description
The intern will be involved in one or more of the following tasks:
- Design and implement deep learning models (e.g., YOLO, CNN, Transformer-based architectures)
- Perform data preprocessing and augmentation for real-world datasets
- Develop real-time inference pipelines on embedded platforms (Jetson Nano/Orin)
- Integrate sensor data (IMU, camera, IoT devices) into AI models
- Conduct performance evaluation (accuracy, latency, FPS, power consumption)
- Assist in research documentation, reporting, and potential publication
- Participate in weekly research meetings and technical discussions
Preferred Intern Educational Level
Senior Undergraduate / Master’s / PhD students
(Background in Electrical Engineering, Computer Science, AI, or related fields)
Skill sets or Qualities
We are looking for candidates with:
Technical Skills:
- Programming in Python
- Experience with Deep Learning frameworks (PyTorch / TensorFlow)
- Basic knowledge of Computer Vision and Machine Learning
- Familiarity with Linux environment
- Understanding of IoT or embedded systems is a plus
Preferred (Not Mandatory):
- Experience with NVIDIA Jetson / Edge AI deployment
- Knowledge of YOLO models or real-time detection systems
- Exposure to signal processing or sensor data analysis
Personal Qualities:
- Strong problem-solving ability
- Self-motivated and proactive learner
- Ability to work independently and in a team
- Good communication skills in English
Internship-related fee required by the school/institution
Students who come to NTPU for an internship are generally charged in the same way as visiting students, on a monthly basis. For details of the fee items, please refer to II. Short-term Internship in NTPU’s visiting student tuition and fees document:
The fees include:
• Monthly fee: NT$3,500–4,109 per month
• Administration fee: NT$1,800
• Computer Usage Fee:
o Bachelor’s Program: NT$2,340
o Master’s & Ph.D. Program: NT$1,990
• Student Insurance: NT$359
Students may decide whether to purchase the insurance based on their needs. The insurance fee may vary slightly each year.
(The above fees do not include accommodation, living expenses, transportation, or other personal expenses.)
If the student’s home university has signed an exchange agreement with NTPU, the student may come to NTPU for an internship by using an exchange quota and may be exempt from tuition fees. However, this arrangement must be discussed and agreed upon by both the student’s home university and NTPU in advance.