Pre-Doctoral Training in Environmental Biostatistics

描述(由申请人提供)： 约翰霍普金斯大学彭博公共卫生学院的生物统计学系提出了一项环境生物统计学博士前培训计划，以支持四(4)名学员。该项目需要两年以上的课程学习，然后是考试和一篇研究论文。培训补助金将在最初的3年内提供；研究助学金将为剩余的培训期提供资金。该项目将设在生物统计学系，并得到生物统计学、环境健康科学、流行病学、卫生服务研究以及分子微生物学和免疫学系教员的支持。通过课程工作、研讨会、参加工作组和指导博士研究，研究人员应教育下一代领导生物统计科学的发展和应用于环境研究和政策。他们应该将生物统计学和生物统计学家与其他环境科学结合起来，在一种非常适合培养生物统计学和其他领域的研究生和教职员工之间持久关系的教育环境中。因此，毕业生将有效地跨学科协作，确定关键的方法需求，然后开发和应用满足这些需求的统计方法。毕业生将有效地向科学家、政策制定者和公众传达实质性的发现。该项目和附属教职员工致力于尊重这一理念并实现这些目标。

DESCRIPTION (provided by applicant): The Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health proposes an Environmental Biostatistics pre-doctoral training program to support four (4) trainees. The program entails two+ years of course work followed by examinations and a research thesis. Training grant support will be provided for the initial 3 years; research assistantships will fund the remaining training period. The program will be located in the Department of Biostatistics and be supported by faculty in the Departments of Biostatistics, Environmental Health Sciences, Epidemiology, Health Services Research, and Molecular Microbiology and Immunology. Through course work, seminars, participation in working groups and directed doctoral research, the investigators shall educate the next generation of leaders in development and application of biostatistical science to environmental research and policy. They shall integrate biostatistics and biostatisticians with other environmental sciences in an educational climate ideally suited to fostering lasting relationships among graduate students and faculty in biostatistics and other fields. As a result, the graduates will effectively collaborate across disciplines, identify the key methodologic needs, then develop and apply statistical approaches that address these needs. Graduates will effectively communicate substantive findings to scientists, policy makers and the general public. Program and affiliated faculty are committed to honoring this philosophy and to achieving these goals.