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

• 批准号: 9024828
• 负责人: THOMAS L. DANIEL
• 金额: $ 28.6万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-23 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9024828
• 关键词: Big Data; Doctor of Philosophy; Genomics; Neurosciences; Training; Universities; Washington

 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(由申请者提供)：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级的研究。然而，大多数生物医学博士前学生并没有接受大数据统计和计算方法方面的尖端培训。此外，大多数统计学和计算机专业的博士前学生没有接受过深入的生物医学科学培训。简而言之，该大学目前缺乏跨越计算、统计学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的重要性与日俱增，这样一个综合计划是迫切需要的。为了培养具有统计学、计算和生物医学专业知识的新一代研究人员，我们建议华盛顿大学从基因组学和神经科学(BDGN)获得大数据博士培训。该项目将专注于生物医学的两个领域，这两个领域的特点都是华盛顿大学的大量数据和广泛的专业知识：基因组学和神经科学。该项目每年将从应用数学、生物学、生物统计学、计算机科学与工程、基因组科学、神经科学和统计学七个博士生项目中吸引六名博士前学生。学员将在其fi第一年或第二年房屋署课程期间获分配培训津贴，并将继续领取培训津贴两年。他们将参加一门严格的课程 这涉及统计学、机器学习和数据科学三门课程，以及基因组学或神经科学的三门课程。每名实习生都将配备两名世界级的教师导师：一名擅长基因组学或神经科学，另一名专门研究大数据的计算或统计方法的开发。该培训计划的其他主要特点包括三个季度的轮换，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，一个暑期实习计划，通过威斯康星州大学的项目举办的世界级暑期课程的机会，同行指导，研讨会，期刊俱乐部，以及关于可重复研究和负责任的研究进行的课程。所有博士前实习生将离开BDGN培训计划，掌握一套核心技能和共同语言，以生成、解释和开发来自基因组学或神经科学的大数据的统计和计算方法。