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Doctor of Philosophy in Biomedical Engineering - Biomedical Imaging and Image Analytics

伦斯勒理工学院

Rensselaer Polytechnic Institute

US美国

  • 学历文凭

    学历文凭

    Ph.D.

  • 专业院系

    专业院系

    Biomedical Engineering

  • 开学时间

    开学时间

  • 课程时长

    课程时长

  • 课程学费

    课程学费

    汇率提示

国际学生入学条件

For admission to graduate study, an applicant must have a U.S. bachelor's degree or the equivalent of a U.S. bachelor's degree prior to enrollment.
Transcripts and Degrees must be in English (all post-secondary education, including transcript keys).
Unofficial transcripts should be uploaded to your graduate application. The minimum requirement for admission consideration is the completion and award of a four year U.S. bachelor's degree, or its equivalent, by the time of planned enrollment.
Personal Statement
Resume or curriculum vitae
Two letters of recommendation
Portfolio, if required by department

TOEFL score of 88 iBT
IELTS score of 6.5
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  • 雅思总分:6.5
  • 托福网考总分:88
  • 托福笔试总分:160
  • 其他语言考试:Duolingo score of 120<br>PTE score of 59

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课程简介

Matriculation into the doctoral program is based upon prior demonstration of a high level of academic achievement in graduate and/or undergraduate work. Advanced study and research are conducted under the guidance of a faculty member of the Department of Biomedical Engineering and an interdisciplinary committee. A total of 72 credits (21 course work credits and 51 credits of research) satisfies the Department's and the Institute's residency and thesis requirements. Students in the Ph.D. program can automatically earn a M.S. degree when completing their candidacy. Also, it is common for doctoral students to receive financial support from the department and/or the faculty.<br><br>Biomedical imaging produces internal images of patients, animals or tissue samples for basic research, preclinical and clinical applications. Biomedical imaging at RPI focus on cutting-edge x-ray, optical tomographic imaging, multi-modality imaging, image formation and analysis as well as artificial intelligence / machine learning methods. Research and training involve the entire process from innovation, instrumentation, to validation for real-world applications. Close collaborative ties have been formed with leading medical schools, and with industrial partners such as the GE Global Research Center, enabling translational impact.
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