Postdoctoral position in Mathematical Modelling of Cognition

Jobs
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The Cognition, Intention, and Action (CoInAct) Research Group and Department of Psychology, Faculty of Social Sciences, University of Copenhagen (UCPH), Denmark, are looking for applications for a Postdoctoral position in mathematical modelling of cognitive processes to be filled by 1 September 2025 or as soon as possible thereafter. The position is for 2 years.
Information about CoInAct can be found at Cognition, Intention and Action (CoInAct) – Department of Psychology - University of Copenhagen.

We are looking for someone with a strong modelling/statistical background who are is expected to be familiar with formals modelling frameworks such as linear ballistic accumulator models, diffusion models, biased competition models, or Bayesian models (e.g., Stan). During the employment, the candidate is expected to engage in the development of computational models, model selection, and contribute to the development of experimental paradigms.

The Postdoc will be part of a project entitled Selection in Cognition funded by the Carlsberg Foundation and headed by Professor Søren Kyllingsbæk and Associate Professor Thor Grünbaum. The larger project develops and tests a new theory of basic cognitive selection mechanisms by combining methods and perspectives from experimental psychology, cognitive neuroscience, mathematical modelling, and philosophy.

Apply here: Postdoctoral position in Mathematical Modelling of Cognition

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Hi, @kyllingsbaek and welcome to the Stan forums. And thanks for posting the job announcement.

This is in my Ph.D. area of cognitive science, but not something I work on these days. Quite a few years ago, I spent a few days trying to fit linear ballistic accumulator models in Stan with some psycholinguists I was visiting, but the resulting models took roughly speaking forever to fit. Is there a way to fit those models more efficiently in a non-Bayesian framework or any way to get a Bayesian posterior efficiently?

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Hi Bob,

Thanks! Good question! Strickland, L., Loft, S., Remington, R. W., & Heathcote, A. (2018). Racing to remember: A theory of decision control in event-based prospective memory. Psychological Review, 125(6), 851–887. APA PsycNet did Bayesian estimation, but only within participants as I remember. As you say it is very time consuming. We would certainly like to give it a try though. This I also why we are looking for someone with those interests!

Best,
Søren