Post-DocApplications Closed

Yashar Ahmadian

Cambridge, United Kingdom
Apply by Jul 1, 2023

Application deadline

Jul 1, 2023

Job

Job location

Yashar Ahmadian

Geocoding

Cambridge, United Kingdom

Geocoding in progress.

Source: legacy

Quick Information

Application Deadline

Jul 1, 2023

Start Date

Flexible

Education Required

See description

Experience Level

Not specified

Job

Job location

Yashar Ahmadian

Geocoding

Cambridge, United Kingdom

Geocoding in progress.

Source: legacy

Map

Job Description

We are seeking a highly motivated and creative postdoctoral researcher to work on a collaborative project between the labs of
Yashar Ahmadian (https://www.cbl-cambridge.org/ahmadian) at the Computational and Biological Learning Lab (CBL -- https://cbl-cambridge.org,
Engineering Department), and Zoe Kourtzi (https://www.abg.psychol.cam.ac.uk/) at the Psychology Department, both at the University of Cambridge.
The project is funded by the UKRI BBSRC and investigates the computational principles and circuit mechanisms underlying
human visual perceptual learning, particularly the role of adaptive changes in the balance of cortical excitation and inhibition
resulting from perceptual learning.

We aim to integrate a few lines of research in our labs, exemplified by the following key publications:

Y Ahmadian and KD Miller (2021). What is the dynamical regime of cerebral cortex? Neuron 109 (21), 3373-3391.
K Jia, ..., Z Kourtzi (2020). Recurrent Processing Drives Perceptual Plasticity. Current Biology 30 (21), 4177-4187.
P Frangou, ..., Z Kourtzi (2019). Learning to optimize perceptual decisions through suppressive interactions in the human brain. Nature Communications 10, 474.
Y Ahmadian, DB Rubin, KD Miller (2013). Analysis of the stabilized supralinear network. Neural Computation 25, 1994-2037.
T Arakaki, GBarello, Y Ahmadian (2019). Inferring neural circuit structure from datasets of heterogeneous tuning curves. PLOS Comp Bio, 15(4): e1006816.

The postdoc will be based in CBL, with free access to the Kourtzi lab in the Psychology department.

Requirements

  • The successful candidate will have:
  • A strong quantitative background.
  • Demonstrable interest in theoretical neuroscience.
  • Obtained (or be close to the completion of) a PhD or equivalent in computational neuroscience
  • machine learning
  • physics
  • computer science
  • mathematics or a related field.
  • Preference will be given to candidates with:
  • Previous experience in computational neuroscience
  • especially with the training of function-optimized neural networks.
  • Sufficient programming skills to run numerical simulations and to use deep learning optimization packages.
  • Expertise with advanced data analysis and Bayesian techniques.