Postdoctoral Associate in Meta-Research - UEIS
Carnegie Mellon University
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Benefits offered by this job
Job summary
Carnegie Mellon University Libraries seeks a Postdoctoral Associate in Meta-Research to develop a research and teaching agenda in meta-research, evidence synthesis, and open science. The role balances 50% research and 50% teaching/community-building, collaborating across CMU units to advance transparent and reproducible research practices.
The successful candidate will lead open science initiatives, teach workshops on evidence synthesis, and contribute to living evidence syntheses using open
Qualifications
- Ph.D. in a STEM or social science discipline.
- Strong interest in building an open science community across disciplinary boundaries and advancing the quality and rigor of research and open scholarship.
- Familiarity with or interest in learning about evidence synthesis and/or bibliometrics methods.
- Experience with coding and programming in R or Python.
- Enthusiasm for working collaboratively with library personnel on research projects, instructional design, and developing services.
Responsibilities
- Conduct research in meta-research and open science, including evidence synthesis and computational methods.
- Teach workshops and develop curriculum on topics like LLM-assisted evidence synthesis and open source tools.
- Serve on organizing committees for open science events and a meta-research hackathon in 2027.
- Assist in outreach for the Libraries' Evidence Synthesis Program and support research tools such as Sysrev and OpenAlex.
- Contribute to international research communities with posters or talks on open synthesis and meta-research.
Skills
Education
Tools
Job description
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.
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, FORCE11, or ESMARConf, with poster presentations or talks on open synthesis and meta-research.
Adaptability, excellence, and passion are vital qualities within the UEIS. We are in search of a team member who can effectively interact with a varied population of internal and external partners at a high level of integrity. We are looking for someone who shares our values and who will support the mission of the university through their work.
- Strong track record in academic research in a discipline that is well established at CMU (relevant research experience in industry or government also considered);
- Demonstrated record of teaching excellence in academic settings including hands-on training or training others in computational skills. Interest in refining teaching skills and curriculum development;
- Experience conducting research on at least one of the following areas: evidence synthesis methods, meta-research, bibliometrics, reproducibility, open science, or scholarly communication;
- Knowledge of conduct and reporting standards for evidence synthesis (e.g., PRISMA);
- Understanding of computational reproducibility best practices;
- Familiarity with machine learning concepts and large language models. Deep expertise is not required, but candidates should be comfortable engaging with these technologies at a foundational level;
- Knowledge of tools used for reproducible research such as Git/GitHub, Jupyter Notebook, Binder, Docker, Open Science Framework;
- Strong interpersonal skills with the ability to effectively interact with diverse groups including faculty, students, staff, and administrators;
- Demonstrated ability to work independently and as part of a team;
- Excellent organizational, communication, and presentation skills;
- Dedication to professional development including personal research and scholarship and growth of skills;
- Interest in contributing to the global open science community.
- Ph.D. in a STEM or social science discipline;
- Strong interest in building an open science community across disciplinary boundaries and advancing the quality and rigor of research and open scholarship;
- Familiarity with or interest in learning about evidence synthesis and/or bibliometrics methods;
- Experience with coding and programming in R or Python. Should be proficient enough to work independently with standard packages and troubleshoot basic issues;
- Enthusiasm for working collaboratively with library personnel on research projects, instructional design, and developing services.
Sponsorship: Applicants for this position must be currently legally authorized to work for CMU in the United States. CMU will not sponsor or take over the sponsorship of an employment visa for this opportunity. Carnegie Mellon is not a qualifying employer for the STEM OPT benefit: only the 12-month OPT may be used to work at Carnegie Mellon.
Fixed Term: This is a fixed-term position with an estimated duration of two years.
Benefits eligible employees enjoy a wide array of benefits including comprehensive medical, prescription, dental, and vision insurance as well as a generous retirement savings program with employer contributions. Unlock your potential with tuition benefits, take well-deserved breaks with ample paid time off and observed holidays, and rest easy with life and accidental death and disability insurance.
Additional perks include a free Pittsburgh Regional Transit bus pass, access to our Family Concierge Team to help navigate childcare needs, fitness center access, and much more!
For a comprehensive overview of the benefits available, explore our Benefits page.
At Carnegie Mellon, we value the whole package when extending offers of employment. Beyond credentials, we evaluate the role and responsibilities, your valuable work experience, and the knowledge gained through education and training. We appreciate your unique skills and the perspective you bring. Your journey with us is about more than just a job; it’s about finding the perfect fit for your professional growth and personal aspirations.
Pittsburgh, PA
Pre/Post-Doctoral Associates & Fellows
Postdoctoral Associate / Fellow (Fixed Term)
Full time
Salary
- Please visit “Why Carnegie Mellon” to learn more about becoming part of an institution inspiring innovations that change the world.
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- Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran.
- Statement of Assurance
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