国际学生入学条件
A bachelors degree or equivalent from an accredited college or university.
To submit and complete your application you must upload a PDF file of your unofficial transcript directly to your online application Unofficial transcripts must be in English.
Please ensure that the scanned documents are readable and include all courses grades ie marks scores etc and credits completed to date The scanned documents should also include information about the institutions grading scale If admitted you will need to provide your official transcript For a transcript to be official it must be in a sealed envelope from the transferring college or university.
Other Documents -
Statement of purpose - 500 words minimum.
Letters of recommendation 2 or 3 for graduate programs.
Curriculum vitae CV Resume.
TOEFL 79, IELTS 6.5, GPA - 3.0
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雅思考试总分
6.5
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雅思考试指南
- 雅思总分:6.5
- 托福网考总分:79
- 托福笔试总分:160
- 其他语言考试:PTE - 53
CRICOS代码:
申请截止日期: 请与IDP顾问联系以获取详细信息。
课程简介
该论文专为有兴趣进行研究的生物工程专业的硕士设计。如果您将来有兴趣继续攻读博士学位,强烈建议您参加此课程。研究和撰写硕士学位论文是一个学术密集的过程,它取代了传统课程作业的8个学分。学生与教师顾问一起选择感兴趣的主题,对该主题进行高级研究,并开发适合在会议上发表或提交给期刊的论文。论文经验为您的硕士学位经验提供了定义,并可以通过展示您的能力来支持您的工作申请或博士学位学习。在非学位论文课程中,您将从课程作业中获得所有所需的36个学时。其中16个必须来自500级课程。没有全面检查。
The Master of Science in Bioengineering program at Northeastern draws on faculty across the University and reflects the significant strengths of bioengineering research in multiple areas.<br><br>Potential career paths: Research Scientist (medical), Biomedical Scientist, Biomedical Engineer, Medical Scientist<br><br>Systems bioengineering focuses on using engineering principles to model and understand complex biological systems, using advanced large-scale experimental technologies and specialized mathematical and computational tools. It involves procuring, manipulating, and analyzing very large data sets to study and understand biological systems. In addition, the acquired understanding of the design principles of biological systems allows design and implementation of synthetic biological systems with required functions for clinical, agricultural, environmental, and energy applications. Topics covered include statistical physics, statistical inference, dynamical and stochastic modeling, execution and analysis of quantitative experimentation, machine learning, and control and information theory. These techniques are taught in the context of biological applications.
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