The Essex Brain-Computer Interfaces and Neural Engineering laboratory is happy to announce a postdoctoral position in the newly funded project “Closed-Loop Multisensory Brain-Computer Interface for Enhanced Decision Accuracy”. The project is supported by the Multidisciplinary University Research Initiative (MURI) programme and it falls within the MURI theme “Modeling and Analysis of Multisensory Neural Information Processing for Direct Brain-Computer Communications” which is jointly funded by the US Department of Defense and the UK Ministry of Defence.
The project is in partnership with the University of Southern California, the University of California Berkeley, Harvard University, New York University, Cold Spring Harbor Laboratory, Imperial College London, and University College London. The award is initially for a three-year base period but may be extended for a further two-year period based on the project successfully achieving its planned outcomes in the first period.
The Essex team will work on brain-computer interfacing, on algorithms for signal processing and extraction of information from EEG and other physiological signals, on behavioural and neuro-physiological investigations of multisensory feature binding and integration, as well as methods for predicting the level of attention and confidence in decision making of a participant from behavioural, physiological and neural data in real time.
Please see full details at:
Feel free to email me ([log in to unmask]) for an informal discussion about this post.
Some of the Essential/Desirable skills are:
# Knowledge and experience of signal processing
# Knowledge and experience in biomedical signal analysis
# A strong publication record
# Significant programming ability
# Experience of designing brain-computer interfaces
# Experience of parametric modelling of perception and/or decision making
# Experience of designing experiments with audio-visual stimulation
# Knowledge of the theory and application of stochastic processes
# Knowledge of Bayesian modelling and inference
# Experience of dynamic causal modelling of EEG data
# Knowledge of inverse modelling and source reconstruction techniques
# Knowledge of techniques for multimodal and multiscale neural signal integration
# Programming ability in high-level numerical computing languages
(see full list at link above)
You may leave the list at any time by sending the command
to [log in to unmask], leaving the subject line blank.