I am an undergraduate student majoring in nuclear technology and nuclear engineering. My program's curriculum was split equally between practical nuclear engineering and nuclear and particle physics courses, both of which were covered in limited depth. What fascinates me most about theoretical nuclear physics is its significant overlap with theoretical high-energy physics (HEP-Th), a field that demands exceptional physical insight amidst abstract mathematics. Through my undergraduate studies, I've come to recognize that my intellectual strengths may not align with the specialized genius required for HEP-Th research.

I began programming at the end of my sophomore year, primarily through self-study to support experimental data analysis in coursework. This led me to explore scientific computing. I also culminated in a project on relativistic hydrodynamics simulation. Although I was unable to complete this project, the experience proved invaluable — it enhanced my technical skills, deepened my understanding of scientific methodology, and crucially, helped me realize that academia might not be my optimal career path. What's more, this realization sparked my growing interest in computational fluid dynamics (CFD).

During my junior year, I secured the recommendation-based postgraduate admission qualification at my institution. However, I found myself uncertain about my future trajectory. Unprepared to immediately enter the workforce after graduation, I needed an opportunity that would enhance my employability rather than prepare me for an academic career. Fortunately, I received an offer in computer science from a Computational Fluid Dynamics (CFD) research group—an ideal solution given the circumstances. This pathway provided me with a master's degree in computer science and a three-year transitional period for career preparation, while aligning with my interests in CFD applications. At that critical juncture, this represented the optimal choice for my professional development.

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