Computational disambiguation of cell type and cell state in single cell RNA-seq data

In this project you will develop methods to distinguish cell type and cell state from single cell RNA-seq data that will be collected in associated experiments.

Illustration

Fig. 1: Workflow for snRNA-seq data analysis.

These data will be compared with measurements of neuron and network activity providing unique insight into cell-type specific computations of behaviors. This will further be explored in neural network simulations. Based on the findings, targeted perturbations will be conducted in vivo and effects will be measured in vivo and compared with in silico simulations. 

You will work in an interdisciplinary environment between physics, computer science and bioscience that combines experiments and modeling, providing a basis for new breakthroughs in our understanding of both artificial and biological neural networks.

A longstanding challenge in neuroscience is to characterize the hundreds of different types of neurons within the brain and understand how they work together to drive behavior.
Recent advances in biological and neurophysiological techniques are finally producing the rich, multidimensional datasets required to meaningfully address this question.
However, in order to gain new insight from these complex datasets, new computational tools and modeling based approaches are desperately needed.

Requirements

You must have a master degree in bioinformatics, neuroscience or similar.

Supervisors

Dr. Jennifer L. Hazen

Professor Gaute Einevoll

Call 2: Project start autumn 2022

This project is in call 2, starting autumn 2022. 

Published Sep. 25, 2020 4:59 PM - Last modified Oct. 14, 2020 10:21 AM