CV
Contact Information
| Name | Chaiyawat Kaewmeechai |
| Professional Title | Postdoctoral Researcher |
| c.kaewmeechai[at]bham.ac.uk | |
| Location | School of Chemistry, Birmingham, B15 2TT |
Professional Summary
Computational materials physicist specialising in defects, disorder and charge trapping in wide-bandgap oxide semiconductors using DFT, molecular dynamics and machine-learning interatomic potentials.
Experience
-
2025 - Birmingham, UK
Postdoctoral Researcher (Supervisor: Prof. David O. Scanlon)
Scanlon Materials Theory Group, University of Birmingham
-
2021 - 2025 London, UK
PhD Researcher (Supervisor: Prof. Alexander L. Shluger)
University College London
Investigated point defects, charge trapping and oxygen interstitials in crystalline and amorphous Ga₂O₃.
-
2019 - 2020 Chiang Mai, Thailand
Research Assistant
Chiang Mai University
Education
-
2021 - 2025 London, UK
PhD
University College London (UCL)
Condensed Matter and Materials Physics
- Defect modelling in crystalline and amorphous Ga₂O₃ (Supervisor: Prof. A. L. Shluger)
-
2017 - 2019 Chiang Mai, Thailand
-
2013 - 2017 Chiang Mai, Thailand
BSc
Chiang Mai University
Physics
Teaching
-
2022 - 2023 London, UK
Computational Physics
University College London
Teaching Assistant (demonstrator).
-
2022 - 2024 London, UK
Electromagnetic Theory
University College London
Teaching Assistant (problem-solving demonstrator, marking).
-
2023 - 2024 London, UK
Practical Physics and Computing 1 (Python)
University College London
Teaching Assistant (demonstrator).
-
2023 - 2024 London, UK
Linear Algebra and Vector Calculus
University College London
Teaching Assistant (problem-solving demonstrator).
Skills
Programming (Advanced): Python, C/C++, Bash, MATLAB, Linux/HPC, SLURM
DFT codes (Advanced): Quantum ESPRESSO, CP2K, ABINIT, VASP
Molecular dynamics (Advanced): LAMMPS, AIMD, machine-learning force fields
Scientific Python (Advanced): NumPy, SciPy, ASE, pymatgen, matplotlib
Languages
Thai : Native speaker
English : Fluent (academic and professional)
Interests
Materials Physics: Defects, polarons, wide-bandgap semiconductors, amorphous solids, machine-learning potentials