PhD Position: Modeling recurrent processing in the visual cortex using deep neural networks

How does recurrent processing shape computation in the visual cortex? Can deep neural networks help explain the dynamics of bottom-up and top-down processing?
For a joint project between the lab of Prof. Hamutal Slovin at the Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, and Dr. Tal Golan at Ben-Gurion University of the Negev, we are seeking an outstanding student for a fully funded, jointly supervised PhD position in computational modeling. The project can also be extended to include new experiments.
The project will use unique voltage-sensitive dye imaging data from macaque visual cortex (V1/V2/V4) to develop deep neural network models of recurrent processing in the visual cortex. The student will work at the intersection of systems neuroscience and deep learning.
The project will involve:
● Developing deep neural network models of bottom-up and top-down processing in the visual cortex
● Using large-scale neural data to constrain and evaluate computational models
● Gaining expertise in state-of-the-art methods for modeling sensory and cognitive computation
● Contributing to an interdisciplinary effort linking cortical dynamics, visual processing, and modern AI
Requirements:
● MSc/MA in a relevant field, including neuroscience, cognitive science, engineering, computer science, or related disciplines; or a BSc with excellent grades (top 10%) qualifying for direct-entry PhD studies. We will also consider students in the final term of their MSc/MA/BSc.
● Strong programming skills in Python and PyTorch
● Background in deep learning through research experience, coursework, or independent projects
● Creativity, initiative, and persistence
● Strong interest in computational neuroscience and interdisciplinary research
To apply, please send:
● CV
● Full grade transcript
● A short paragraph describing your interest in the project
Deadline for applying: 18 April 2026
Applications may be submitted here:
https://forms.gle/CPaDQR2aAXri898F9