大学生面试时英语自我介绍

大学生面试时英语自我介绍

    英语面试时如何突出口语的优秀呢？英语口语自我介绍既要简单又要突出重点，怎样才能做到这点。以下是一位英语高手在面试时英语自我介绍的资料：

　　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, 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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