Postdoctoral Associate in Meta-research - UEIS

Are you a recent Ph.D. graduate in a STEM or social science discipline interested in a career path in meta-research, knowledge synthesis, open science and/or improving the quality and rigor of research? Join the team of Evidence Synthesis specialists and the Open Science Program at Carnegie Mellon University Libraries, where we are working to investigate and promote open science principles within evidence synthesis and meta-research methods as well as computational approaches to improving the efficiency, transparency, reproducibility, and accessibility of research across disciplines.

The Postdoctoral Associate in Meta-Research should be technically and computationally skilled, have a strong understanding of academic research and the research ecosystem and be passionate about building an open and collaborative research community. The successful candidate will develop a research and teaching agenda related to meta-research, evidence synthesis, open science, and research improvement using open source tools and technologies. This is a fixed term 2-year position with 50% of time devoted to research and 50% to teaching and community-building activities.

Meta-research is the study of how research is conducted, communicated, evaluated, and synthesized, with the goal of improving the quality, transparency, efficiency, and impact of research. One emerging area within meta-research is open synthesis, which combines open science principles with evidence synthesis methods such as systematic reviews, scoping reviews, and evidence and gap maps. With the exponential growth of scholarly publishing, increasing demand for evidence-informed decision making, and rapid advances in artificial intelligence and large language models, there is a growing need to investigate questions at the intersection of meta-research, open science, and evidence synthesis. This includes research and capacity-building efforts related to:

• Improving the openness, accessibility, and interoperability of scholarly information and bibliographic metadata;

• Developing and evaluating open source software, AI tools, and computational workflows that support evidence synthesis and other forms of meta-research;

• Advancing transparency, reproducibility, and reporting standards for research and evidence synthesis;

• Developing living evidence syntheses and workflows for keeping research knowledge current using open bibliographic data and automated methods.

Carnegie Mellon University is well-poised to advance meta-research with one of the first library-based Open Science programs, an Open Source Programs Office, one of the leading Computer Science programs in the world, and a unique Evidence Synthesis Program with deep faculty-librarian collaborations across the university.  The Postdoctoral Associate in Meta-Research will work across these departments and units to develop a variety of research and training initiatives.

Core responsibilities will include:

Under the supervision of the Director of the Evidence Synthesis Program, the successful candidate will:

• Conduct collaborative research in meta-research, including topics such as: transparency and reproducibility in evidence synthesis; computational methods for literature discovery, study selection, and data extraction; evaluation of AI and open source tools that support meta-research workflows; living evidence synthesis methods; and/or bibliographic infrastructure;

• Teach workshops and develop curriculum on topics such as using large language models for evidence synthesis, using open source software to manage and conduct literature review, and/or the ethical and appropriate use of scholarly information in meta-research;

• Serve on organizing and programming committees of open science and evidence synthesis events hosted by the Libraries, including the annual Open Science Symposium and an inaugural meta-research hackathon to be held in Spring 2027;

• Assist in the outreach efforts of the Libraries' Evidence Synthesis Program including the support of research tools such as Sysrev and OpenAlex;

• Contribute to international research communities, such as Metascience,