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University of Chicago - Grossman Center for Quantitative Biology and Human Behavior
The Grossman Center for Quantitative Biology and Human Behavior at the University of Chicago seeks outstanding applicants for multiple postdoctoral positions in computational and theoretical neuroscience.
Arvind Kumar
We are interested in understanding how the basal ganglia and the cerebellum interact during a sensori-motor task. To this end we use both experimental data (multiunit activity and behavior) and computational models. On one hand we will record multiunit neuronal activity in the cerebellum and basal ganglia while animals perform a motor task. On the other hand we will use computational models to understand how activity in one brain region affects the representation of task related activity in the other area. More info: https://www.kth.se/profile/arvindku/page/postdoctoral-researcher-position
Yashar Ahmadian
The postdoc will work on a collaborative project between the labs of Yashar Ahmadian at the Computational and Biological Learning Lab (CBL), and Zoe Kourtzi at the Psychology Department, both at the University of Cambridge. The project 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. The postdoc will be based in CBL, with free access to the Kourtzi lab in the Psychology department.
Dr Andrej Bicanski
This project involves modelling the staggered development and the decline with age of spatial coding in the mammalian brain, as well as data analysis of single neuron recordings. The position is based at Newcastle University, UK, with a rotation in the lab of Prof. Colin Lever in Durham, UK. The project is fully funded for 4 years by the BBSRC. Both international and UK students can apply, and fees are covered.
Prof. Tatjana Tchumatchenko
Postdoc position: The postdoc candidate will be involved in a computational project addressing how neurons efficiently synthesize and distribute proteins in order to ensure that these are readily available across all synapses, will analyze data and model synaptic plasticity changes in order to understand health and disease states computationally. This work is centered on computational tools and includes pen-and-paper calculations, data analysis, and numerical simulations and requires an interdisciplinary mindset. PhD position: The PhD candidate will be conducting circuit level data analysis and modeling of neural activity states. He/she will contribute to the development of machine learning algorithms to analyse imaging data or to distinguish different behavioral activity states. This work is centered on dynamical systems methods, data analysis and numerical simulations and requires an interdisciplinary mindset. Master students interested in conducting Master thesis research (6-12 months) related to the two projects above a welcome to apply.
Dr. Udo Ernst
In this project we want to study organization and optimization of flexible information processing in neural networks, with specific focus on the visual system. You will use network modelling, numerical simulation, and mathematical analysis to investigate fundamental aspects of flexible computation such as task-dependent coordination of multiple brain areas for efficient information processing, as well as the emergence of flexible circuits originating from learning schemes which simultaneously optimize for function and flexibility. These studies will be complemented by biophysically realistic modelling and data analysis in collaboration with experimental work done in the lab of Prof. Dr. Andreas Kreiter, also at the University of Bremen. Here we will investigate selective attention as a central aspect of flexibility in the visual system, involving task-dependent coordination of multiple visual areas.
Dr. Udo Ernst
The Computational Neurophysics lab at the University of Bremen headed by Dr. Udo Ernst offers at the earliest date possible: Postdoc / PhD student in Computational Neuroscience for 3 years. In this project we want to study organization and optimization of flexible information processing in neural networks, with specific focus on the visual system. You will use network modelling, numerical simulation, and mathematical analysis to investigate fundamental aspects of flexible computation such as task-dependent coordination of multiple brain areas for efficient information processing, as well as the emergence of flexible circuits originating from learning schemes which simultaneously optimize for function and flexibility. These studies will be complemented by biophysically realistic modelling and data analysis in collaboration with experimental work. Here we will investigate selective attention as a central aspect of flexibility in the visual system, involving task-dependent coordination of multiple visual areas.
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