面试时英语自我介绍

时间：2024-08-31 14:02:18 求职英语

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：

面试时英语自我介绍

　　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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