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: 9339731
• 负责人: THOMAS L. DANIEL
• 金额: $ 30.36万
• 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/9339731
• 关键词: Big Data; Doctor of Philosophy; Neurosciences; Training; Universities; Washington; genomic data; Big Data; Doctor of Philosophy; Neurosciences; Training; Universities; Washington; genomic data

 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（由适用提供）：华盛顿大学在大数据分析的开发以及生物医学研究的许多领域进行世界一流的研究。但是，大多数生物医学科学专业的学生都没有接受大数据统计和计算方法的最先进培训。此外，大多数统计和计算领域的专业学生都无法接受生物医学科学的深入培训。简而言之，该大学目前缺乏跨越计算，统计和生物医学科学的综合培训计划。鉴于在生物医学研究的许多领域中，大数据的重要性越来越重要，因此需要这种综合计划。为了培训具有统计，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的基因组学和神经科学（BDGN）的大数据培训。该计划将重点放在生物医学科学的两个领域，这两种领域的特征是华盛顿大学的大量数据以及广泛的专业知识：基因组学和神经科学。该计划将从以下七个博士学位课程中每年吸引六个学生：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。学员将在高清研究的第一年或第二年期间任命培训补助金，并将继续培训两年。他们将参加严格的课程 这涉及统计，机器学习和数据科学的三个课程，以及基因组学或神经科学的三个课程。每个学员将与两位世界一流的教师指导者配对：一位专门研究基因组学或神经科学，第二个专门研究大数据的计算或统计方法。该培训计划的其他关键特征包括三个四分之一的轮换，至少一个专注于基因组学或神经科学以及一个专注于统计或计算方法，暑期实习计划，参加世界一流的夏季课程的机会，通过UW计划，同伴心理，半手赛，期刊，日记俱乐部，日记俱乐部以及负责任的研究和负责任的研究和对负责任的研究进行研究。所有现代培训人员都将为BDGN培训计划提供一套核心技能以及从基因组学或神经科学生成大数据的统计和计算方法所需的核心技能和通用语言。