Characterizing Residual Stress on Pipework Weld Repairs Using Machine Learning.

As I rounded the curve while maintaining my pace within the green area of the floor, I was
shocked to see a massive red-hot ingot being lowered by rails onto a platform after emerging from
a building-sized furnace. I closed in on the delegation as we continued our factory tour at Doosan
Heavy Industries' nuclear reactor production division in Changwon, South Korea, attempting not
to be distracted by the surrounding welding activities. Participating in that two-week intensive
IAEA-KHNP training programme on the successful launch of nuclear power programmes for
newcomer countries while I was employed as a technologist at the Ghana nuclear power institute
prompted a period of intense self-reflection regarding my purpose, something I had struggled to
determine up until that point. Engaging with like-minded engineers and researchers from around
the world as they shared knowledge in the spirit of cooperation to advance nuclear energy, I quickly
realized that this was a field to which I was more suited and to which I would be content devoting
myself to making significant contributions. Consequently, despite having a bachelor's degree in
biomedical engineering, I decided to pursue master's studies in nuclear engineering.
Having conducted research during both my undergraduate and graduate studies, as well as
spending two years analyzing data in a nuclear research institution and completing certification
courses on Udemy, I have acquired skills in data science, machine learning with Pytorch, molecular
dynamics, Monte Carlo simulation, and scientific writing, among others. All of these inform my
approach to problem-solving and allow me to construct research projects, conduct experiments
and simulations, and derive meaningful insights from output data-all of which are crucial to
research. For example, my master's thesis required me to develop custom data modules with
Python scripts to automate the process of reading specific numerical values from simulation
logfiles and processing, computing, and graphing the results. Consequently, by reducing repetitive
workflows, I had more time to focus on result interpretation.