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Alison Preston

Professor

University of Texas at Austin

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United States

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Research Interests

Neuropsychology

10%

Psychology

10%

Cognitive Neuroscience

10%

Biology

10%

Cognitive Science

10%

Learning And Memory

10%

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Positions1

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Alison Preston

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The University of Texas at Austin

Postdoctoral Fellows in Cognitive Neuroscience, Learning and Memory, and Computational Cognitive Science at The University of Texas at Austin

The Preston Lab at The University of Texas at Austin is recruiting 1–2 postdoctoral fellows to join research on how people build structured knowledge from experience and use that knowledge to guide flexible behavior. The lab studies learning and memory across multiple domains, including space, time, associative learning, and naturalistic event understanding . The research aims to explain how knowledge-derivation processes support reasoning, prediction, inference, and generalization when goals or environments change. A major emphasis is the use of computational approaches , including AI-based models , alongside behavioral experiments , fMRI , computational modeling, and naturalistic stimuli. The lab also has a strong lifespan focus, including NIH-funded longitudinal studies of how memory representations change from adolescence into emerging adulthood. The PI is especially interested in candidates who want to use the postdoctoral position as a springboard toward an independent research program. Postdocs are expected to take ownership of projects, shape new research directions, lead manuscripts, build collaborations, and develop grant-writing and mentoring skills. Applicants may come from cognitive neuroscience, psychology, developmental science, computational cognitive science, AI/ML-informed behavioral modeling , or related fields. Prior experience with every method or population is not required; intellectual fit, methodological curiosity, and a strong commitment to rigorous science are the main priorities. To apply, send a CV , a statement of research interests , and the names of three references to [email protected] . Questions can also be sent by email or LinkedIn message.