The lab is looking for a motivated postdoctoal research fellow as part on our multi-university project to investigate the neural underpinnings of causal inference.
The successful candidate will work on robust implementations of neural data analysis and visualization methods, based on contemporary approaches in probabilistic machine learning, and will develop new data analysis methods, again based on probabilistic machine learning, that are suitable for the large, high-dimensional, neural datasets generated by the project. They will also lead the initial setup, and continual customization of the DataJoint-based data neural data sharing and analysis platform that will be used by all project members.
We expect suitable applicants to have a Doctorate in a related discipline (including neuroscience, computer science, engineering, physics, or similar). We also expect good knowledge of the Python programming language, as well as experience in Unix/Linux, and the use of databases, in particular SQL. Expertise in handling and analyzing high-dimensional data, especially neural data, would be a plus, as would be experience in the implementation and use of probabilistic machine learning methods/models. The successful candidate will also enjoy, and be good at, working in teams at the interface of experimental and theoretical neuroscience.