Graduate Training Program in Computational Biology, Bioinformatics and Biomedical Data Science (CBB)

项目总结 在高通量实验生物学、生物医学的数字化转型和 高性能计算、机器学习和数据科学的突破创造了关键需求- 以及一个前所未有的机会--在界面上培训下一代多学科研究人员 生物医学和计算科学。这项提议将建立一个创新的、包容的、跨学科的 计算生物学、生物信息学和生物医学数据科学(I3CBB)T32培训计划。I3CBB 该计划将以：(I)整合生物医学和计算科学的多学科研究培训为特色；(Ii) 一项跨校园计划，整合了不同科学学科的学生，并利用研究基础设施 生物信息学和计算生物学中心和数据科学研究所；(3)包容性和 协作的团队科学环境，具有基于证据的、可重复的和负责任的研究行为 确保科学的严谨性和公正性；以及(Iv)针对不同的职业抱负和社会需求进行劳动力发展 冲击力。I3CBB项目汇集了30名不同职业阶段和不同性别的项目教师 和种族背景，来自特拉华大学的十个系，以及特拉华州的附属机构 大学。受训者将由具有活跃研究和互补专业知识的教师共同指导 专题研究领域，采用数学、计算和数据科学方法进行多尺度 对生物网络的系统级理解，从分子序列和结构到细胞功能， 生理学以及与环境的相互作用。从符合资格的第二年开始，每年将招聘六名学员 为博士生提供为期一年的T32资助，并结合使用大学资金的招聘战略 支持将最强大的博士前候选人带到校园，重点放在代表性不足的群体上， 从而在五年期间为总共30名受训人员提供了变革性的培训。课程设置将 包括科学的技术能力培训和专业培训，以培养领导力和 团队合作。学员将完成四门核心课程，涵盖定量/计算方法， 技术、实验设计和数据解释，有个性化的学习计划和个人 发展规划。I3CBB开发核心的三个课程将涵盖负责任的行为、可重复性、 道德和多样性，并包括一个以团队为基础的体验式学习课程，强调团队合作， 沟通和创新。第二年将以为期10周的暑期实习为高潮，共同设计 与外部学术界、工业界、政府合作伙伴。所有实习生、教师导师和我们的合作伙伴将组成 I3CBB社区，并参与每周研讨会、研讨会、年度研讨会、黑客松等 团队建设活动。由执行委员会、计划委员会和咨询委员会组成的组织结构将增强 疏忽。总体而言，这款T32将为变革性的劳动力形成一个协作的团队科学基础设施 发展。

PROJECT SUMMARY Convergent advances in high-throughput experimental biology, digital transformation of biomedicine, and breakthroughs in high-performance computing, machine learning and data science have created critical needs– and an unprecedented opportunity–to train the next generation of multidisciplinary researchers at the interface of biomedical and computational sciences. This proposal would build an Innovative, Inclusive, Interdisciplinary Computational Biology, Bioinformatics, and Biomedical Data Science (i3CBB) T32 training program. The i3CBB program will feature: (i) multidisciplinary research training integrating biomedical and computational sciences; (ii) a cross-campus initiative integrating students across scientific disciplines, and leveraging research infrastructure at the Center for Bioinformatics and Computational Biology and the Data Science Institute; (iii) an inclusive and collaborative team science environment with evidence-based, reproducible, and responsible research conduct to ensure scientific rigor and equity; and (iv) workforce development for diverse career aspirations with societal impact. The i3CBB program brings together 30 program faculty with different career stages and diverse gender and ethnic backgrounds from ten departments at the University of Delaware, and affiliates from Delaware State University. The trainees will be co-mentored by faculty with vibrant research and complementary expertise in thematic research areas, employing mathematical, computational, and data science approaches for multi-scale systems-level understanding of biological networks, from molecular sequence and structure to cellular function, physiology, and interaction with environments. Six trainees will be recruited annually from eligible second year PhD students for one-year T32 funding support, coupling with a recruitment strategy using university funds to support bringing the strongest predoctoral candidates to campus with a focus on underrepresented groups, thereby providing transformative training for a total of 30 trainees during the 5-year period. The curriculum will encompass scientific training for technical competency and professional training to develop leadership and teamwork. Trainees will complete four core courses covering topics of quantitative/computational methods, technology, experimental design, and data interpretation, with individualized program of study and individual development plan. A three course i3CBB Development Core will cover responsible conduct, reproducibility, ethics, and diversity, and include a team-based experiential learning course emphasizing teamwork, communication, and innovation. Year 2 will culminate in a 10-week summer internship designed in collaboration with external academia, industry, government partners. All trainees, faculty mentors, and our partners will form the i3CBB community and engage in weekly seminars, workshops, annual symposia, hackathons, and other team-building events. An organizational structure with executive, program, and advisory committees will enhance oversight. Collectively, this T32 will form a collaborative team science infrastructure for transformative workforce development.