[CogSci] Postdoc in the ManyBabies Project

[Michael C. Frank]

Postdoctoral Fellowship with the ManyBabies Project

The ManyBabies Project <http://manybabies.stanford.edu/> is an effort to
build large-scale international collaborations in infancy research, with
the joint goals of replication, best practices development, and
theory-building. We address the big questions in early development through
bringing many labs together to design strong studies, collect big datasets,
and conduct state-of-the art analyses. ManyBabies 1 and 1-Bilingual, which
we believe to be the largest-ever experimental studies of infant
development, are already in progress. More than 50 labs are collecting data
on infants’ preference for infant directed speech, with data collection
finishing this spring. ManyBabies 2 is currently in the design phase, and
subsequent studies are being planned as well.

We are looking for a postdoctoral fellow to assist with the ManyBabies
project, specifically in planning, coordination, and data analysis. The
position will be based in the Stanford Language and Cognition Lab
<http://langcog.stanford.edu/>, under the supervision of Michael Frank but
with many opportunities for collaboration with the broader ManyBabies
network including researchers on the the governing board. The primary
research focus of this position is the development and application of
statistical analyses to the rich and multi-faceted datasets emerging from
the ManyBabies project. Funding for this position is guaranteed for two
years but there is the possibility of extension to a third year pending
external funding.

This fellowship offers rich opportunities for the postdoc to establish
their own, independent line of research. Some of the following give an idea
of the possible areas of investigating depending on the fellow’s specific
interests: 1) developing meta-analyses of the target phenomena and
advancing meta-analytic methods for theoretical synthesis (following, e.g.,
some of the tools at http://metalab.stanford.edu), 2) developing
computational models of learning and attention using the large ManyBabies
datasets involving habituation/familiarization (following work on Bayesian
learning models for infancy data, e.g., Frank & Tenenbaum, 2010), or 3)
meta-scientific investigations of reproducibility and replicability in
infancy.

All applicants to the position are welcome, but ideal applicants will
likely have some combination of the following qualifications:

   -

   Expertise in child development ideally but not necessarily focusing on
   infancy research;
   -

   Strong statistical and analytical skills, including regression
   (especially mixed effects models) and bayesian methods;
   -

   Experience with meta-science methods, e.g. meta-analysis, p-curve, etc.;
   -

   Good communication and coordination skills in service of interacting
   with a large and diverse group of researchers around the world; and
   -

   Strong programming skills and facility with open science tools (e.g.,
   Open Science Framework, github, RMarkdown or Jupyter notebooks).

Start date for the position is flexible but would be ideally be before
September 2018; funds are available now but we are willing to wait for the
best candidates.


The ManyBabies project values inclusiveness and encourages candidates that
bring personal diversity of all types ot the position. We recognize that
many otherwise strong candidates will lack one or more of the skills listed
above and are prepared to provide appropriate training opportunities.
Stanford is an unparalleled environment for work at the intersection of
child development and reproducibility. Weekly seminar series in cognition
and in development provide opportunities for learning and feedback Stanford
also offers many training opportunities in statistical and computational
methods through coursework and collaboration. Further, the ManyBabies
Governing Board is enthusiastic to interact with and mentor the postdoc as
well.


To apply, please send to dkellier at stanford.edu with subject “ManyBabies
Postdoc Application”:

   -

   CV,
   -

   Brief coverletter stating interest and qualifications for the position,
   -

   Links to shared analytic materials that demonstrate qualifications
   (e.g., github page, OSF link), and
   -

   Names for 2-3 references.
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