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        求職時英語自我介紹

        時間:2024-02-11 09:50:07 自我介紹 我要投稿
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        求職時英語自我介紹

          處在一個陌生場合時,我們往往需要進行適當?shù)淖晕医榻B,用自我介紹往往可以向他人介紹自己。到底應如何寫自我介紹呢?以下是小編整理的求職時英語自我介紹,歡迎大家分享。

        求職時英語自我介紹

          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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