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Research Scientist position at Indiana University's Cognitive Development Lab focused on creating and analyzing a shareable dataset from nearly 10,000 children to understand interactions between visual cognition and early vocabulary development. The role will leverage advanced computational methods to identify developmental pathways and inform future longitudinal studies.

Key Responsibilities: Assist in creation and analysis of a large cross-sectional dataset of already-collected developmental data using computational and statistical methods. Lead a team of undergraduate and master's students in applying machine learning and graph theoretic approaches to discover developmental trajectories and underlying factors.
Skills & Tools: Strong quantitative and computational training with excellent programming skills, proficiency in statistical methods, machine learning, and/or graph theoretics. Demonstrated research track record, initiative, team leadership abilities, and experience in behavioral science, particularly in development, language, or visual cognition.
Qualifications: PhD in Psychology or related field required. Prior experience in computational/statistical analyses, behavioral research, and ideally developmental, language, or visual cognition domains.
Location: In-Person
Compensation: $120K–$180K/yr (estimated)

Job Description

Research Scientist for Indiana University Cognitive Development Lab

The Cognitive Development Lab at Indiana University-Bloomington (PI: Linda Smith) invites applications for a Research Scientist to assist in the creation and analysis of a shareable data set (of already collected data) from nearly 10,000 children ages 15 to 36 months. There is both a scientific premise and a methodological premise underlying the project.

The scientific premise is that developmental changes in visual cognition and early vocabulary development interact, with advances in visual cognition supporting early language learning and also being changed by language learning. The literature indicates multiple causal factors and inflection points that can disrupt this early stage of vocabulary growth— limitations in audition, or vision, in phonological representations, in visual attention, in the composition of early vocabularies, and in the quality and composition of language input.

The methodological premise concerns how to leverage all the data that has been already collected on these issues; experiments with many of the same overlapping measures designed with specific hypotheses in mind. The methodological premise for this project is that given some overlapping measures in the individual data sets and through the use of advanced analytic tools including machine learning and graph theoretics, one can discover multiple developmental pathways in cross-sectional data and the factors that underlie the trajectories of those pathways. These inferred pathways then can be empirically tested in longitudinal studies.

Strong applicants will have quantitative and computational training as well as experience in behavioral science, ideally in development, language, or visual cognition.
The successful applicant will have:
• Excellent research track record.
• Excellent programming skills.
• Experience in computational and statistical methods and analyses including either graph theoretic of machine learning approaches (or both).
• A track record of initiative, ability to lead a team (of undergraduates/masters students) and effective teamwork.
• Fit of this project with their own career trajectory/goals
• PhD in Psychology or related field


How to Apply: Interested candidates should apply at
https://indiana.peopleadmin.com/postings/26344
by submitting a cover letter describing your interests and prior experiences, CV, and contact information for three references. Applications will be accepted on an ongoing basis until the position is filled. Application review will start December 1, 2024. Anticipated start date is February 1, 2025. Start date is negotiable.

Questions regarding the position or application process can be directed to: Dr. Linda Smith (smith4@iu.edu).

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