PhD or Postdoc Position on Computational Modeling of Active Auditory Perception

The project will address how the brain simultaneously adapts sensory coding strategies, attentional processing, and behavior to optimize active auditory perception in its recurrent processing architecture. The project is embedded in Germany’s priority program “Sensing LOOPS: Cortico-subcortical Interactions for Adaptive Sensing“(www.brainloops.de/spp2411) and will provide ample opportunities for collaboration with experimental labs, including the lab of Julio Hechavarrria in Frankfurt (https://www.julio-hechavarria.com). Keywords: active perception, active efficient coding, active noise avoidance, recurrent neural network, attentional modulation, bat echolocation, cocktail party problem.

Please see some of our previous work on active visual and auditory perception and use of recurrent networks to model visual object recognition to get a flavor of this line of work:

• Eckmann, S., Klimmasch, L., Shi, B. E., & Triesch, J. (2020). Active efficient coding explains the development of binocular vision and its failure in amblyopia. Proceedings of the National Academy of Sciences, 117(11), 6156-6162.

https://www.pnas.org/doi/abs/10.1073/pnas.1908100117

    • Wijesinghe, L. P., Wohlgemuth, M. J., So, R. H., Triesch, J., Moss, C. F., & Shi, B. E. (2021). Active head rolls enhance sonar-based auditory localization performance. PLoS computational biology, 17(5), e1008973.

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008973

    • Ernst, M. R., Burwick, T., & Triesch, J. (2021). Recurrent processing improves occluded object recognition and gives rise to perceptual hysteresis. Journal of Vision, 21(13), 6-6.

https://jov.arvojournals.org/article.aspx?articleid=2778154

    • Klimmasch, L., Schneider, J., Lelais, A., Fronius, M., Shi, B. E., & Triesch, J. (2021). The development of active binocular vision under normal and alternate rearing conditions. Elife, 10, e56212.

https://elifesciences.org/articles/56212

The Frankfurt Institute for Advanced Studies (https://fias.institute/en/) is a research institution dedicated to fundamental theoretical research in various areas of science. The city of Frankfurt is the hub of one of the most vibrant metropolitan areas in Europe. It boasts a rich culture and arts community and repeatedly earns high rankings in worldwide surveys of quality of living.

We are seeking an outstanding and highly motivated post-doc for this project. Applicants should have obtained a Master or PhD in Computational Neuroscience or a related field (Machine Learning, Physics, Engineering, etc.). The ideal candidate will have excellent analytic and neural network modeling skills (in particular using unsupervised and reinforcement learning techniques), a thorough understanding of information theory and a broad knowledge of Computational Neuroscience. A strong interest in active auditory perception and desire to collaborate with experimental labs is essential.

Renumeration is according to the German E13 pay scale. Details depend on the applicant’s experience.

Applications should consist of a single pdf file. Please include a brief statement of research interests, CV and publication list, and contact information for at least two references. Application deadline is February 29, 2024. Upload your document using the application platform at: https://pm.fias.science/projects/application. Click on „New Issue“ to start the application process