We are seeking a proactive and highly motivated Postdoctoral Research Associate to join the Oxford Neurotheory Lab headed by Dr Andrew Saxe to work on a project investigating the principles of learning in distributed brain networks.
The post is full-time and fixed-term for 2 years.
The role lies at the interface of theoretical neuroscience and deep learning theory and will involve conducting research into artificial deep networks using techniques from applied mathematics, with applications to biological and engineered systems. A particular focus is on understanding how internal representations change over time in multi-layered systems, in order to develop ways of empirically testing the links between modern machine learning methods and the brain and mind. The successful candidate will have the opportunity to bring theoretical ideas in contact with experiment through collaboration with a variety of experimentalists and the broader Neurotheory community at Oxford.
The successful candidate will hold or expect to hold a PhD in theoretical neuroscience or a closely related discipline with a demonstrable interest in mathematical analysis of artificial neural network models. You will have a proven track record of publishing work as a lead author relating to the theory of deep learning or theoretical neuroscience along with strong quantitative and programming skills. Excellent communication skills, along with a collaborative outlook are also essential for this role. A demonstrable interest in human and/or animal psychology and neuroscience with a track record of designing experimental paradigms, analysing data, and/or conducting advanced statistical analyses would also be desirable.
Applications for this vacancy are to be made online. You will be required to upload a supporting statement and CV as part of your online application.
The closing date for applications is 12.00 midday on 15 April 2020. It is anticipated that interviews will be held on 24 April 2020.