Materials Science Researcher

Jingkai Bo

Jingkai Bo

Jingkai Bo

Ph.D. Candidate · JST SPRING Scholar

Materials science researcher working on microscopy, defect analysis, and image-based methods.

📍 Tokyo, Japan
🎓 Tokyo University of Science
🌍 Oxford (Jul–Oct 2026)
Get in Touch
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About

I am a Ph.D. candidate in Materials Science and Technology at Tokyo University of Science, specializing in microscopy-based defect analysis and deep learning-enhanced image processing. My research focuses on understanding materials behavior at the nanoscale through advanced characterization techniques, particularly transmission electron microscopy (TEM) and scanning electron microscopy (SEM).

Currently supported by the JST SPRING scholarship, I am pursuing innovative approaches to automate defect detection and reconstruction. I will be joining the University of Oxford as a Visiting Researcher from July to October 2026, where I will collaborate on advanced microscopy research.

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Skills & Focus Areas

Materials Science
STEM Image Processing
Defect Reconstruction
Deep Learning
MD Simulation
XPFC Simulation
Python
C++
TEM Operation
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Education & Experience

2022 – Present
Ph.D. Candidate · Materials Science and Technology
Tokyo University of Science
Jul 2026 – Oct 2026
Visiting Researcher
University of Oxford
Collaborative research on advanced microscopy and materials characterization
2020 – 2022
M.S. · Quantum Physics
Kyushu University
2016 – 2020
B.S. · Information Management and Information Systems
Liaoning Technical University
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Publications

Identification of miners' unsafe behaviors based on transfer learning and residual network
China Safety Science Journal · Volume 30 · Pages 41-46 · 2020
Tingxin Wen, Guitong Wang, Xiangbo Kong, Mengxiao Liu, and Jingkai Bo
Real-time in-situ three-dimensional observation of dislocations during tensile deformation
Materials Characterization · Volume 221 · 114725 · March 2025
Yifang Zhao, Hongye Gao, Jingkai Bo, Zimeng Guo, Qi Zhang, Yiming Ma, and Satoshi Hata
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Presentations

Novel Denoise Based on ResNet for STEM Observation
Poster · 79th Annual Meeting MSJ · Matsue, Japan · June 2023
Development of a real-time in-situ three-dimensional observation method for dislocation dynamics — Student Best Presentation Award
Oral · 65th Kyushu Branch Academic Meeting MSJ · Kitakyushu, Japan · December 2023
Enhanced Denoising of STEM Images with Deep Learning
Poster · IMC20 Satellite Symposium · Fukuoka, Japan · September 2023
Real-time in-situ three-dimensional observation of dislocations — Best Presentation Award
Oral · Joint Academic Meeting of JIM/ISIJ/JIM Kyushu Branches · Kyushu University, Fukuoka, Japan · June 2024
Real-time in-situ three-dimensional observation of dislocations during tensile deformation
Oral · International Symposium on Microscopy & Microanalysis 2024 · Fukuoka, Japan · September 2024
Time-series 3D reconstruction of Co-doped BaFe₂As₂ sintering process in SEM
Oral · International Symposium on Microscopy & Microanalysis 2024 · Fukuoka, Japan · September 2024
Phase-Field Crystal Coupled Model for Cyclic Single-Crystal Copper
Poster · 2026 Spring Meeting of JIM · Tokyo, Japan · March 2026
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Intellectual Property

Development of an Automated Approach to the Design of Material Functions via Precise Control of Lattice Defects
Patent · 2025-0076 · 2025
Inventors: Jingkai Bo, Xiao-Wen Lei, Toshiyuki Fujii
AI Search Engine Retrieval System V1.0
Registered Software · No. 2019SR0859414 · 2019
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Recognition & Awards

  • JST SPRING Scholar
    Japan Science and Technology Agency
  • Outstanding Graduate
    Liaoning Provincial Department of Education · 2021
  • Registered Software
    AI Search Engine Retrieval System V1.0 · 2019