[Comp-neuro] Fully-funded PhD Position in Computational and Cognitive Neuroscience

Alireza Soltani alireza.soltani at gmail.com
Sun Nov 15 05:14:31 CET 2020


Fully-funded PhD Position in Computational and Cognitive Neuroscience

The Computational and Cognitive Neuroscience Lab (CCNL) at Dartmouth has a fully-funded PhD position for a motivated student to work on an exciting project on the role of attention in reward-based learning. The project involves a combination of human experiments and computational modeling.
Experimentally, it aims to extend our previous studies that examined learning with multiple cues (Soltani et al, 2016) and multi-dimensional cues (Farashahi et al, 2017; Farashahi et al, 2020). In terms of computational modeling, the aim is to extend current mechanistic models (c.f., Soltani et al, 2016 and Farashahi et al, 2017) as well as recurrent neural networks to better understand the role of attention in learning from high-dimensional stimuli and how complex learning strategies emerge over time.

A suitable candidate would have a strong background in cognitive and/or computational neuroscience, good computer programming skills, and a strong desire to understand neural mechanisms underlying cognition.

Interested candidates should contact Alireza Soltani (Alireza.Soltani at dartmouth.edu).  
To apply, please visit the Dartmouth PBS’s Graduate Admissions page <https://pbs.dartmouth.edu/menufeature/graduate/graduate-admissions>
For more recent publications from the lab please visit http://ccnl.dartmouth.edu <http://ccnl.dartmouth.edu/>

Relevant references:
1. Soltani A, Khorsand P, Guo CZ, Farashahi S, Liu J (2016). Neural Substrates of Cognitive Biases during Probabilistic Inference. Nature Communications, 7:11393
2. Farashahi S, Rowe K, Aslami Z, Lee D, Soltani A (2017). Feature-based Learning Improves Adaptability without Compromising Precision. Nature Communications, 8:1768.
3. Farashahi S, Xu J, Wu S-W, Soltani A (2020). Learning Arbitrary Stimulus-Reward Associations for Naturalistic Stimuli Involves Transition from Learning about Features to Learning about Objects. Cognition, 205.

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