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University of Washington PhD Training in Big Data for Genomics and Neuroscience

华盛顿大学基因组学和神经科学大数据博士培训

基本信息

批准号：
9765397
负责人：
THOMAS L. DANIEL
金额：
$ 22.42万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-23 至 2021-11-15
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9765397
关键词：
Big Data Doctor of Philosophy Neurosciences Training Universities Washington genomic data

项目摘要

DESCRIPTION (provided by applicant): The University of Washington conducts world-class research in the development of big data analytics, as well as in many areas of biomedical research. However, most predoctoral students in biomedical science do not receive cutting-edge training in statistical and computational methods for big data. Furthermore, most predoctoral students in statistics and computing do not receive in-depth training in biomedical science. In short, the university currently lacks an integrated training program that spans computation, statistics, and biomedical science. Given the growing importance of big data across many areas of biomedical research, such an integrated program is critically needed. In order to train a new generation of researchers with expertise in statistics, computing, and biomedical science, we propose the University of Washington PhD Training in Big Data from Genomics and Neuroscience (BDGN). This program will focus on two areas of biomedical science, both of which are characterized by huge amounts of data as well as extensive expertise at the University of Washington: genomics and neuroscience. The program will draw six predoctoral students per year from the following seven PhD programs: Applied Mathematics, Biology, Biostatistics, Computer Science & Engineering, Genome Sciences, Neuroscience, and Statistics. Trainees will be appointed to the training grant during their ﬁrst or second year of hD studies and will continue on the training grant for two years. They will take a rigorous curriculum that involves three courses in statistics, machine learning, and data science, and three courses in either genomics or neuroscience. Each trainee will be paired with two world-class faculty mentors: one specializing in either genomics or neuroscience, and a second specializing in the development of either computational or statistical methods for big data. Other key features of the training program include three one-quarter rotations, with at least one focusing on genomics or neuroscience and one focusing on statistical or computational methods, a summer internship program, opportunities to attend world-class summer courses run through UW programs, peer mentoring, seminars, journal clubs, and courses on reproducible research and on responsible conduct of research. All predoctoral trainees will leave the BDGN Training Program with a core set of skills and a common language required for generating, interpreting, and developing statistical and computational methods for big data from genomics or neuroscience.

描述（由申请人提供）：华盛顿大学在大数据分析的发展以及生物医学研究的许多领域进行世界一流的研究。然而，大多数生物医学科学的博士生没有接受大数据统计和计算方法的尖端培训。此外，大多数统计学和计算机专业的博士预科生没有接受过生物医学科学方面的深入培训。简而言之，该大学目前缺乏一个涵盖计算，统计和生物医学科学的综合培训计划。鉴于大数据在生物医学研究的许多领域日益重要，这样一个综合计划是迫切需要的。为了培养具有统计学，计算和生物医学科学专业知识的新一代研究人员，我们提出了华盛顿大学基因组学和神经科学大数据博士培训（BDGN）。该计划将侧重于生物医学科学的两个领域，这两个领域的特点是大量的数据以及华盛顿大学的广泛专业知识：基因组学和神经科学。该计划每年将从以下七个博士课程中吸引六名博士生：应用数学，生物学，生物统计学，计算机科学与工程，基因组科学，神经科学和统计学。学员将在其第一年或第二年的hD学习期间获得培训补助金，并将继续获得两年的培训补助金。他们将接受严格的课程其中包括三门统计学、机器学习和数据科学课程，以及三门基因组学或神经科学课程。每个学员将与两名世界级的教师导师配对：一名专门从事基因组学或神经科学，另一名专门从事大数据计算或统计方法的开发。培训计划的其他主要特点包括三个季度轮换，其中至少一个侧重于基因组学或神经科学，一个侧重于统计或计算方法，暑期实习计划，参加世界一流的暑期课程的机会通过华盛顿大学计划，同行指导，研讨会，期刊俱乐部，以及可重复研究和负责任的研究行为的课程。所有博士前学员将离开BDGN培训计划，获得一套核心技能和一种通用语言，用于生成，解释和开发基因组学或神经科学大数据的统计和计算方法。