We currently are seeking a postdoctoral research associate (see ad below) and at least one PhD student. To apply for a PhD position, check details at https://langcog.psychology.uconn.edu and/or send an email to Jim Magnuson.
Postdoctoral Research Associate
Computational neuroscience of human speech recognition
Profs. James Magnuson and Jay Rueckl at the University of Connecticut, Department of Psychological Sciences, seek a postdoctoral research associate for a project focused on computational approaches to understanding the cognitive and neurobiological bases of human speech recognition. Our primary objective is bridging the gap between current cognitive models of human speech processing (such as TRACE) and cutting-edge deep learning models used for automatic speech recognition. The postdoctoral research associate will contribute to this project by playing a leading role in our computational work (devising, developing, and testing neural network models, and comparing them to human behavior and neurobiology), while having room to lead new projects related to this theme.
The University of Connecticut (UConn) is home to a vibrant, interdisciplinary community of researchers in the brain and cognitive sciences, including more than a dozen labs with primary focus on language. UConn and the Magnuson and Rueckl labs are committed to increasing diversity in the STEM workforce.
• PhD in psychology, neuroscience, linguistics, computer science, engineering, or a related field
• High level of expertise in Python and R
• Strong record of research (as demonstrated by presentations and publications)
• Evidence of ability to complete projects
• Evidence of strong writing skill
• Experience with TensorFlow
• Experience with another framework for neural network modeling
• Experience with Java or other programming languages
• Training and experience in empirical research with human subjects
• Hardware proficiency (e.g., ability to select components for and assemble a workstation for deep learning)
Start date for this position is anticipated for Fall, 2019, or early 2020. The position is for an initial one-year appointment and is potentially renewable until June, 2021. Continuation beyond June, 2021 is contingent upon future external funding applications. Salary for this position will be determined by NIH guidelines ($50,004 annually minimum).
Please apply online via UConn Jobs (https://hr.uconn.edu/jobs/), Staff positions, Search #2020123. For full consideration, candidates should submit a resume or CV, a personal statement describing their research interests and goals, and up to three supplemental PDF documents that reflect their academic writing (e.g., journal publications, Master’s thesis, or dissertation). Three letters of recommendation should be emailed directly from letter writers to Dr. James Magnuson (email@example.com). Screening of applicants will begin immediately.
Employment of the successful candidate is contingent upon the successful completion of a pre-employment criminal background check. (Search #2020123)
This job posting is scheduled to be removed at 11:59 p.m. on November 15, 2019.
All employees are subject to adherence of the State Code of Ethics, which may be found at http://www.ct.gov/ethics/site/default.asp.
The University of Connecticut is committed to building and supporting a multicultural and diverse community of students, faculty, and staff. The diversion of students, faculty, and staff continues to increase, as does the number of honors students, valedictorians and salutatorians who consistently make UConn their top choice. More than 100 research centers and institutes serve the University’s teaching, research, diversity, and outreach missions, leading to UConn’s ranking as one of the nation’s top research universities. UConn’s faculty and staff are the critical link to fostering and expanding our vibrant, multicultural and diverse University community. As an Affirmative Action/Equal Employment Opportunity employer, UConn encourages applications from women, veterans, people with disabilities, and members of traditionally underrepresented populations.