The Knowledge Lab <https://www.knowledgelab.org/> at the University of
Chicago seeks to hire an outstanding candidate for a postdoctoral research
opportunity with support from the National Science Foundation to understand
the nature of peer review and improve it for the many purposes to which it
is applied in the modern scientific enterprise (e.g., grant allocation,
conference participation, manuscript selection). The project, titled
“Optimizing Scientific Peer Review” responds to the realization that
reviewers are influenced by innumerable biases, including those instilled
through training; stemming from personal allegiances; inculcated by
experience; colored by race, gender, career stage, and status; and
resulting from direct commitments to (or against) an idea, framework, or
style. These complicating factors make the match of reviewers to submitted
manuscripts a critical factor for review outcome. The match between
reviewers and manuscripts, however, has rarely been analyzed and has never
been optimized for minimizing bias and maximizing quality. This project
will analyze the effect of reviewer choice and review pool composition on
review outcomes, design field review experiments to identify discovered
effects, and deploy algorithms to improve the reviewer matching process.
Consider an early publication from the program here
<https://www.sciencedirect.com/science/article/pii/S0048733318301598?via%3Dihub>
.
Postdoctoral candidates will design and conduct independent research, in
collaboration with UChicago Professor and Knowledge Lab Director James
Evans <https://www.knowledgelab.org/people/detail/james_a_evans/>, Daniel
Acuña <https://ischool.syr.edu/people/directories/view/deacuna/>, a
Computer and Information Scientist from Syracuse University, and Konrad
Körding <http://koerding.com/>, a computational neuroscientist and data
scientist from the University of Pennsylvania. Candidates much have
substantial computational and data science background and a Ph.D. in
Sociology, Economics, Psychology or a related Social/Behavioral Science,
Linguistics, Physics, Applied Math, Computer Science, Information Science
or a related field, and a strong publishing background.
Specifically, the successful candidate will be responsible to (1) manage
large-scale scientific publication and review data; (2) develop features
capturing scientific collaborations and citation networks, manuscript
contents, and the profiles of manuscript authors, editors and reviewers;
(3) use statistical and machine learning approaches to quantify the impact
of reviewer-manuscript matches on review outcomes; and (4) generate and
design experiments to evaluate reviewer recommendations that seek to
optimize reviewer assignment for the benefit of the scientific enterprise
(e.g., increasing novelty; reducing time to publication, minimizing bias).
Review experiments will be in collaboration with publishers (e.g., PLOS
ONE), conferences (e.g., Cosyne), and potentially funding agencies.
Candidates must have experience with Statistical Modeling, Machine Learning
(ML) and Natural language Processing (NLP) techniques. Candidates would
benefit from knowledge regarding state of the art in network analysis and
NLP (e.g., neural language models, context free grammars and auto
encoders). Candidates must have knowledge of scientific computing in
Python. Positions could begin anytime within the coming year, and as early
as September 2018. Competitive salary & benefits.
To apply, please send CV and names for letters from at least two references
to Candice Lewis, [log in to unmask]
Candice Lewis, Ph.D.
Assistant Director
The Knowledge Lab <https://www.knowledgelab.org/>
University of Chicago
5735 S Ellis Ave| Room 221
Chicago, IL 60637
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