*** Apply before 20 December 2017 ***
Applications are invited for an experienced and enthusiastic cognitive neuroscientist to work on an ERC Advanced Investigator grant on the neurobiology of language comprehension using statistical language modelling techniques on MEG neuroimaging and fMRI data-sets.
The applicant will join the Centre for Speech, Language and the Brain (at the University of Cambridge http://cslb.psychol.cam.ac.uk), an inter-disciplinary team led by Professor L.K. Tyler. The Centre has access to a research-dedicated 3T Siemens MR scanner, and EEG and MEG facilities. We currently use a variety of analysis methods on MEG data-sets, and in particular multivariate analysis methods such as Representational Similarity Analyses on MEG data.
Applicants must have a PhD in computer science, cognitive neuroscience or a related discipline and an interest in language processing. Candidates are expected to have advanced statistical skills, experience in statistical language modelling and/or machine learning and excellent computer programming (especially MATLAB and/or Python) skills. Experience of MEG analyses, particularly in using multivariate neuroimaging methods for MEG data and MEG source localization methods and knowledge of the neurobiology of language processing are highly desirable. Relevant post-doctoral experience is also desirable. Given the nature of the research, candidates should be fully fluent in English.
Fixed-term: The funds for this post are available for 2 years in the first instance, with the possibility of extension until 30 September 2020. Please quote reference PJ14037 on your application and in any correspondence about this vacancy.
Further information on the vacancy, and information on how to apply online can be found here: http://www.jobs.cam.ac.uk/job/15786/
Informal enquiries can be made to Professor Lorraine Tyler (email@example.com). Please quote reference PJ14037 on your application and in any correspondence about this vacancy.
The closing date for applications is 20 December 2017.