Quantitative Biomedical Sciences at Dartmouth

 描述（由申请人提供）：请求对达特茅斯学院定量生物医学科学（QBS）的多学科博士前培训项目提供支持。QBS计划成立于2010年，旨在提供定量，计算和生物医学科学的交叉培训，为学生准备一个新的研究领域，该领域越来越多地以复杂问题，大数据和多学科团队为特征。QBS课程覆盖整个达特茅斯学院校园，包括来自文理学院、商学院、工程学院和医学院的教师。QBS每年招收6-8名来自不同背景的学生，包括计算机科学，数学，生物，化学和工程。这些学生是从一个高素质的国家和国际候选人池中挑选出来的，这些候选人具有丰富的民族和种族多样性。作为拟议的T32培训计划的一部分，我们将每年选择两名高素质的博士前学生，根据学历和论文项目提供为期两年的支持，该项目将侧重于大数据。博士前学员将完成八门课程的核心课程，其中包括两门计算机科学或数据科学课程，两门生物信息学课程，两门生物统计学课程，两门流行病学课程和两门综合生物医学科学课程序列，使学生接触跨学科研究，并在协作大数据类项目中达到高潮。该计划的学生参加两门选修课程，一个学期的教学，期刊俱乐部和研讨会，负责进行研究的培训，书面和口头资格考试，年度研究进展研讨会，以及完成一个重要的研究项目，形成了口头答辩的书面论文的基础。教师培训师包括QBS和计算机科学教师，他们拥有校外资金，研究生培训经验的良好记录，大数据研究的良好记录以及参与成功的博士前培训所需活动的承诺。学院的研究领域包括生物信息学、生物统计学、计算机科学、计算生物学、基因组学和流行病学。我们的愿景是，下一代大数据研究人员需要来自生物信息学、生物统计学、计算机科学和流行病学等领域的多种技能和专业知识，以汇集研究人员团队。

 DESCRIPTION (provided by applicant): Support is requested for a multidisciplinary predoctoral training program in Quantitative Biomedical Sciences (QBS) at Dartmouth College. The QBS program was founded in 2010 to provide cross-training in quantitative, computational and biomedical sciences to prepare students for a new research landscape that is increasingly characterized by complex problems, big data and multidisciplinary teams. The QBS program spans the entire Dartmouth College campus including faculty from the Schools of Arts and Sciences, Business, Engineering and Medicine. QBS admits 6-8 students per year from different backgrounds including computer science, mathematics, biology, chemistry, and engineering. These students are selected from a highly qualified national and international pool of candidates that are rich in both ethnic and racial diversity. As part of the proposed T32 training program, we will select two highly qualified predoctoral students each year for two years of support based on academic qualifications and a dissertation project that will focus on big data. The predoctoral trainees will complete an eight course core curriculum that will include two courses in computer science or data science, two courses in bioinformatics, two courses in biostatistics, two courses in epidemiology and a two course sequence in integrative biomedical sciences that exposes students to interdisciplinary research and that culminates in a collaborative big data class project. Students in this program take two elective courses, one term of teaching, journal clubs and seminars, training in responsible conduct of research, a written and oral qualifier exam, a yearly research in progress seminar, and the completion of a significant research project that forms the foundation of the written dissertation that is orally defended. Faculty trainers include QBS and computer science faculty that have extramural funding, a strong track record of graduate training experience, a strong track record of big data research and a commitment to participating in the activities necessary for successful predoctoral training. Research areas of the faculty include bioinformatics, biostatistics, computer science, computational biology, genomics, and epidemiology. It is our vision that the next generation of big data researchers need multiple skills and expertise from areas such as bioinformatics, biostatistics, computer science and epidemiology to bring together teams of researchers.