Job Posting

Title Postdoctoral Position in Understanding and Improving Peer Review
Company Knowledge Lab, UChicago

The Knowledge Lab 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.
Postdoctoral candidates will design and conduct independent research, in collaboration with
UChicago Professor and Knowledge Lab Director James Evans, Daniel Acuña, a Computer and
Information Scientist from Syracuse University, and Konrad Körding, 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, cover letter and names for letters from at least two references to Candice

Location Searle Chemistry Laboratory
Deadline 27 June 2019

Candice Lewis

Job Type Full Time
Attachment Postdoc_NSF_Peer_Review.pdf