Looking for a Post Doctoral Research Associate to collaborate at the development of Bayesian (DCM and Active Inference) computational models of multimodal social interaction taking into account the role of human chemosignals perception. This will involve also the development of robust algorithms for signal processing, statistical inference and extraction of information from EEG and other physiological signals, as well as the design and implementation of software for the execution of experiments with adaptive VR stimulation.
Application closing date 02/01/2019
The School of Computer Science and Electronic Engineering, the Department of Psychology, and the Essex Brain-Computer Interfaces and Neural Engineering Lab are pleased to announce this postdoctoral position in the Horizon 2020 project “POTION: Promoting social interaction through emotional body odours”. The project will last five years and start in January 2019 and includes partners from the Universities of Pisa (Italy), Padova (Italy), and Essex (UK), the Universitat Politecnica De Valencia (Spain), the Katholieke Universiteit Leuven (Belgium), and the Karolinska Institutet (Sweden), and three companies ISPA CRL (Portougal), SRA Instruments (France) and Feel-Ing s.r.l. (Italy). POTION proposes a novel technological paradigm to delve deeper into understanding meaningful social interaction, combining new knowledge about the chemical composition of human social chemosignals together with a novel olfactory-based technology designed to drive social behaviour.
Duties of the Role
The Essex team’s work on the project focuses on the development of Bayesian (DCM and Active Inference) computational models of multimodal social interaction. This models will be applied to evaluate socially relevant variables, such as trust, presence and inclusion as well as to generate optimal stimuli in artificially mediated social interactions. In particular, the models will cover the role of human chemosignal perception in social interactions. The models will be identified and tested using neurophysiological data (e.g. EEG), peripheral physiological activation (i.e., ECG, RESP, EDA) and behavioural changes (i.e., f-EMG) collected using VR scenarios of increasing complexity. The successful applicant will research and develop Bayesian (DCM and Active Inference) computational models of multimodal social interaction with an emphasis on the role of human chemosignals. They will also develop robust algorithms for signal processing, statistical inference and extraction of information from EEG and other physiological signals, design and implement software for the execution of experiments with VR stimulation, and contribute to the reporting and dissemination of the project.
Skills and qualifications required
Applicants are expected to hold a PhD (or be close to completion) in Computational Neuroscience, Brain-computer Interfaces, Neural Engineering, Psychology, Machine Learning, Statistics, Physics, Mathematics, Computer Science or a closely related discipline, or equivalent professional experience or practice. The ideal candidate will have significant experience in computational modelling of social interaction, signal processing, statistical modelling of neural signals and processes, brain-computer interfaces, and virtual reality interfaces. Applicants are also expected to have a strong publication record (relative to their career stage) as first author, ideally including publications in 1st quartile journals in relevant areas. We strongly encourage women to apply as they are currently under-represented in the School of Computer Science and Electronic Engineering.
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