国际学生入学条件
The IEOR PhD program is designed for students with an interest in pursuing advanced studies in Industrial Engineering and Operations Research. Prospective applicants come from a diverse set of backgrounds including Math, Applied Math, Statistics, Computer Science, Electrical Engineering and Operations Research.
Students entering the doctoral program must demonstrate proficiency in the following areas: Real Analysis, Linear Algebra, Probability and Statistics.
The basic requirement for admission as a graduate student is the bachelor's degree received from an institution of acceptable standing. Ordinarily, the applicant will have majored in the field in which graduate study is intended, but in certain programs preparation in a related field of engineering or science is acceptable. The applicant will be admitted only if the undergraduate record shows promise of productive and effective graduate work. The Master of Science (MS) degree is required for admission into the PhD and EngScD degree programs. A student who holds an appropriate bachelor's degree in engineering may apply for admission to either the MS only or MS leading to PhD program.
International applicants or applicants whose undergraduate degree was received in a country in which English is not the official and widely spoken language must submit the following additional requirements with the application for admission:
1. Test of English as a Foreign Language (TOEFL), International English Language Testing System (IELTS) or Pearson Test of English (PTE Academic)
2. Translation of the official transcript(s) and degree/diploma certificate(s) if the institution(s) attended does not issue transcripts in English. The translation must be conducted by a reputable service provider.
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雅思考试总分
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雅思考试指南
- 雅思总分:6
- 托福网考总分:60
- 托福笔试总分:160
- 其他语言考试:NA
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申请截止日期: 请与IDP顾问联系以获取详细信息。
课程简介
Stochastic modeling and its primary computational tool, simulation, are both essential components of Operations Research that are built upon probability, statistics, and stochastic processes to study complex physical systems. Such systems often take the form of a large-scale network of interconnected resources, such as the Internet, power/utility grids and other critical infrastructures, airline networks, global supply chains, hospitals and healthcare systems. Key problems of interest include: how to take measurement, evaluate system performance, and manage resources, how to assess risk and implement hedging and mitigation strategies, how to make decisions that are often required to be real-time, adaptive, and decentralized, and how to conduct analysis and optimization that are effective and robust, including wherever necessary using approximations and asymptotics. Basic tools and methodologies in this area closely interact and overlap with those in financial engineering, business analytics, machine learning, optimization, and computation. IEOR faculty with research/teaching interests in this area regularly collaborate with colleagues in other engineering and science departments and Columbia Business School, and play an active role in the Data Science Institute.
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