英语自我介绍参考范本

英语自我介绍参考范本

　　优秀英文自我介绍范文

　　在中，如何将自己的学业成果和工作经验用巧妙的语言让面试官第一时间接受呢？请看下列的：

　　General Introduction

　　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.