Post-DocApplications Closed
Yashar Ahmadian
Cambridge, United Kingdom
Position Details
Apply by Jul 1, 2023
Post-Doc
Cambridge, United Kingdom
Application deadline
Jul 1, 2023
Job
Job location
Cambridge, United Kingdom
Quick Information
Application Deadline
Jul 1, 2023
Start Date
Flexible
Education Required
See description
Experience Level
—
Job
Job location
Cambridge, United Kingdom
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.
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.
Job
Job location
Coordinates pending.
Cambridge, United Kingdom
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