Computational Materials Physics • Scientific Software • Enterprise AI Support
Hyun-Jung Kim
Ph.D. in Theoretical Condensed Matter Physics
Senior Research Engineer · Computational Materials Physicist
My background spans computational materials physics, OLED materials R&D, scientific software, and enterprise AI support at LG Display.
Current responsibilities include AI project review, technical scouting, internal GitLab platform operation, GitLab-based MLOps support, and internal education for AI and AX adoption.
AI project review, quantum-technology coordination, GitLab operation, and internal AI lectures
Current responsibilities cover AI initiative review, emerging technology scouting, internal GitLab platform operation, GitLab-based MLOps support, and internal AI lectures for AI and AX teams. In parallel, I serve as the coordinator representing LG Display in a group-level quantum-technology working group.
Governance / Quantum WG
AI project review and quantum-technology coordination
I prepare and review AI initiative materials, including project scope, expected deliverables, review records, risks, and follow-up items. As the coordinator representing LG Display, I review quantum-computing technologies, define candidate application problems, and help advance selected topics toward project formulation.
GitLab Operations
Internal GitLab platform operation and MLOps enablement
Responsibilities include internal GitLab platform management and operation, repository structures, collaboration workflows, deliverable tracking, and GitLab-based MLOps practice support.
Education
Internal AI lectures and adoption
Training materials and sessions cover AI/ML fundamentals, workflow automation, LLM application development, AX execution, and Git/GitLab collaboration.
AI Enablement Assets
Selected public artifacts for education, participation, and review.
Selected public artifacts related to scientific software, education sites, GenAI workflow harnesses, serverless hosting examples, and AI technology review notes.
AI Education
AI education materials and lecture sites
Web-based materials and lectures covering AI fundamentals, machine learning, AI/ML mathematics, LLM application development, AX execution, and Git/GitLab collaboration.
A React/Vite-based event operations platform for real-time voting, message boards, quizzes, and lucky draws, deployed and operated with Cloudflare Workers / Durable Objects.
A local prototype for organizing Remotion scenes, Runway API wrappers, generation logs, review gates, budgets, and tool-assisted handoffs for reproducible video-production experiments.
I use AI-assisted writing workflows to turn source material on AI technologies, frontier models, agent systems, and materials AI into readable technical reviews.
The timeline lists graduate study, postdoctoral research, industrial materials R&D, and the current enterprise AI support role.
2025–Present
AI Governance Team, LG Display
Senior research engineer working on AI project review, technical scouting, internal education, and GitLab-MLOps support; coordinator representing LG Display in a group-level working group for quantum-computing technology review, application-problem definition, and project-formulation efforts.
2022–2024
Materials/Device Development AI Task, LG Display
Applied quantum-chemical and DFT-driven analysis to molecular design problems, linking computation, mechanism analysis, and data-driven screening for industrial R&D.
2020–2022
PGI/IAS, Forschungszentrum Jülich
Visiting scientist and postdoctoral researcher at PGI-1 and IAS-1, supported by the Humboldt Research Fellowship, working on electronic structure, topology, and transferable modeling.
Division of Computational Sciences & QUC, Korea Institute for Advanced Study (KIAS)
Postdoctoral researcher and research fellow in computational sciences and the Quantum Universe Center, developing theory-driven workflows for low-dimensional and topological materials.
M.S. and Ph.D. in theoretical condensed matter physics, building the academic foundation in electronic structure, low-dimensional systems, and surface science.
#AI-for-materials, #phase-transition, #topological-materials, #chiral-CDW, and #scientific-software, built around first-principles, quantum-chemical, and tight-binding workflows.
TBFIT, VASPBERRY, and VASPBAUM as reusable tools for tight-binding, topology, and VASP analysis.
Open-source research workflows designed around interpretable electronic-structure calculations.
Future Work
Quantum-computing approaches for materials calculation and inverse design
Planning work on how quantum-computing methods could be connected to materials property calculation, candidate search, and inverse-design workflows. The current focus is problem framing, feasibility review, and hybrid quantum-classical workflow design rather than completed implementation.
The CV and linked profiles contain the fuller academic and project record. Areas I can discuss include AI project review and adoption support, GitLab-based workflows, materials modeling, and quantum-computing feasibility review.