The University of Glasgow has PhD studentships available for 2020 entry through SOCIAL, the UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents, which include the following imaging-related research projects:
Evaluating and Shaping Cognitive Training with Artificial Intelligence Agents (Supervisors: Dr Fani Deligianni and Dr Monika Harvey)
- This project aims to exploit machine learning to develop intuitive measures of cognitive training in a platform independent way. Objectives include: 1) Predicting psychological dimensions (ie. enjoyment, anxiety, valence and arousal) based on performance and neurophysiological data; 2) Relating performance improvements (ie. learning rate) to psychological dimensions and physiological data (ie. EEG and eye-tracking); 3) Developing artificial intelligence approaches that are able to modulate the virtual reality world to control learning rate and participant satisfaction.
Modulating Cognitive Models of Emotional Intelligence (Supervisors: Dr Fani Deligianni and Prof Frank Pollick)
- This project aims to exploit real-time functional Magnetic Resonance Imaging (fMRI) neurofeedback techniques to build cognitive models that explain modulation of brain activity in key regions related to empathy and emotions. Overall aims are: 1) To build data-driven cognitive models of real-time brain network interaction during emotional modulation via neurofeedback techniques; 2) To develop advanced machine learning algorithm to perform cross-domain learning between fMRI and EEG; 3) To develop intelligent artificial agents based on portable EEG systems to successfully regulate emotional responses, taking into account cognitive models derived in the fMRI scanner.
Detecting Affective States based on Human Motion Analysis (Supervisors: Dr Fani Deligianni and Dr Marios Philiastides)
- This project aims to investigate the relationship between effective mental states and psychomotor abilities with relation to gait, balance and posture while emotions are modulated via augmented reality displays. The goal is to develop a comprehensive continuous map of interrelationships in both normal subjects and subjects affected by a mood disorder. Challenges to be addressed include: 1) Building robust experimental setup of intuitive naturalistic paradigms; 2) Developing AI algorithms to relate neurophysiological data with gait characteristics based on state-of-the-art motion capture systems (taking into account motion artefacts during gait); 3) Developing AI algorithms to improve detection of gait characteristics via rgbd cameras and possibly new assistive living technologies based on pulsed laser beam.
For more information on the studentships, please visit: https://socialcdt.org/program/
Application deadline: 30th June 2020
,
The University of Glasgow has PhD studentships available for 2020 entry through SOCIAL, the UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents, which include the following imaging-related research projects:
Evaluating and Shaping Cognitive Training with Artificial Intelligence Agents (Supervisors: Dr Fani Deligianni and Dr Monika Harvey)
- This project aims to exploit machine learning to develop intuitive measures of cognitive training in a platform independent way. Objectives include: 1) Predicting psychological dimensions (ie. enjoyment, anxiety, valence and arousal) based on performance and neurophysiological data; 2) Relating performance improvements (ie. learning rate) to psychological dimensions and physiological data (ie. EEG and eye-tracking); 3) Developing artificial intelligence approaches that are able to modulate the virtual reality world to control learning rate and participant satisfaction.
Modulating Cognitive Models of Emotional Intelligence (Supervisors: Dr Fani Deligianni and Prof Frank Pollick)
- This project aims to exploit real-time functional Magnetic Resonance Imaging (fMRI) neurofeedback techniques to build cognitive models that explain modulation of brain activity in key regions related to empathy and emotions. Overall aims are: 1) To build data-driven cognitive models of real-time brain network interaction during emotional modulation via neurofeedback techniques; 2) To develop advanced machine learning algorithm to perform cross-domain learning between fMRI and EEG; 3) To develop intelligent artificial agents based on portable EEG systems to successfully regulate emotional responses, taking into account cognitive models derived in the fMRI scanner.
Detecting Affective States based on Human Motion Analysis (Supervisors: Dr Fani Deligianni and Dr Marios Philiastides)
- This project aims to investigate the relationship between effective mental states and psychomotor abilities with relation to gait, balance and posture while emotions are modulated via augmented reality displays. The goal is to develop a comprehensive continuous map of interrelationships in both normal subjects and subjects affected by a mood disorder. Challenges to be addressed include: 1) Building robust experimental setup of intuitive naturalistic paradigms; 2) Developing AI algorithms to relate neurophysiological data with gait characteristics based on state-of-the-art motion capture systems (taking into account motion artefacts during gait); 3) Developing AI algorithms to improve detection of gait characteristics via rgbd cameras and possibly new assistive living technologies based on pulsed laser beam.
For more information on the studentships, please visit: https://socialcdt.org/program/
Application deadline: 30th June 2020
,
The University of Glasgow has PhD studentships available for 2020 entry through SOCIAL, the UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents, which include the following imaging-related research projects:
Evaluating and Shaping Cognitive Training with Artificial Intelligence Agents (Supervisors: Dr Fani Deligianni and Dr Monika Harvey)
- This project aims to exploit machine learning to develop intuitive measures of cognitive training in a platform independent way. Objectives include: 1) Predicting psychological dimensions (ie. enjoyment, anxiety, valence and arousal) based on performance and neurophysiological data; 2) Relating performance improvements (ie. learning rate) to psychological dimensions and physiological data (ie. EEG and eye-tracking); 3) Developing artificial intelligence approaches that are able to modulate the virtual reality world to control learning rate and participant satisfaction.
Modulating Cognitive Models of Emotional Intelligence (Supervisors: Dr Fani Deligianni and Prof Frank Pollick)
- This project aims to exploit real-time functional Magnetic Resonance Imaging (fMRI) neurofeedback techniques to build cognitive models that explain modulation of brain activity in key regions related to empathy and emotions. Overall aims are: 1) To build data-driven cognitive models of real-time brain network interaction during emotional modulation via neurofeedback techniques; 2) To develop advanced machine learning algorithm to perform cross-domain learning between fMRI and EEG; 3) To develop intelligent artificial agents based on portable EEG systems to successfully regulate emotional responses, taking into account cognitive models derived in the fMRI scanner.
Detecting Affective States based on Human Motion Analysis (Supervisors: Dr Fani Deligianni and Dr Marios Philiastides)
- This project aims to investigate the relationship between effective mental states and psychomotor abilities with relation to gait, balance and posture while emotions are modulated via augmented reality displays. The goal is to develop a comprehensive continuous map of interrelationships in both normal subjects and subjects affected by a mood disorder. Challenges to be addressed include: 1) Building robust experimental setup of intuitive naturalistic paradigms; 2) Developing AI algorithms to relate neurophysiological data with gait characteristics based on state-of-the-art motion capture systems (taking into account motion artefacts during gait); 3) Developing AI algorithms to improve detection of gait characteristics via rgbd cameras and possibly new assistive living technologies based on pulsed laser beam.
For more information on the studentships, please visit: https://socialcdt.org/program/
Application deadline: 30th June 2020