面試時(shí)英語自我介紹
英語面試時(shí)如何突出口語的優(yōu)秀呢?英語口語自我介紹既要簡單又要突出重點(diǎn)，怎樣才能做到這點(diǎn)。以下是一位英語高手在面試時(shí)英語自我介紹的資料：
I am a third year master major in automation at Shanghai Jiao Tong University, p. R. China. With tremendous interest in Industrial Engineering, I am writing to apply for acceptance into your ph.D. graduate program.
Education background
In 1995, I entered the Nanjing University of Science & Technology (NUST) -- widely considered one of the China’s best engineering schools. During the following undergraduate study, my academic records kept distinguished among the whole department. I was granted First Class prize every semester,In 1999, I got the privilege to enter the graduate program waived of the admission test.
At the period of my graduate study, my overall GpA(3.77/4.0) ranked top 5% in the department. In the second semester人才網(wǎng), I became teacher assistant that is given to talented and matured students only. This year, I won the Acer Scholarship as the one and only candidate in my department, which is the ultimate accolade for distinguished students endowed by my university. presently, I am preparing my graduation thesis and trying for the honor of Excellent Graduation Thesis.
Research experience and academic activity
When a sophomore, I joined the Association of AI Enthusiast and began to narrow down my interest for my future research. With the tool of OpenGL and Matlab, I designed a simulation program for transportation scheduling system. It is now widely used by different research groups in NUST. I assumed and fulfilled a sewage analysis & dispose project for Nanjing sewage treatment plant. This was my first practice to convert a laboratory idea to a commercial product.
In retrospect, I find myself standing on a solid basis in both theory and experience, which has prepared me for the ph.D. program. My future research interests include: Network Scheduling problem, Heuristic Algorithm research (especially in GA and Neural network), Supply chain network research, Hybrid system performance analysis with petri nets and Data Mining.
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