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  Turst Detection from Brain Signals in Human-AI Teaming


   Department of Computer and Information Sciences

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Are you passionate about the future of human-AI collaboration? Do you want to explore the intricate dynamics of trust between humans and artificial intelligence systems? We invite applications for a PhD position in the exciting field of trust detection from brain signals in human-AI teaming.

 Project Overview:

In an era where human-AI collaboration is becoming increasingly prevalent, understanding and enhancing trust between humans and AI systems is crucial. This interdisciplinary research project aims to investigate trust dynamics in human-AI teams using advanced neuroscience techniques. By analysing brain signals, such as electroencephalography (EEG), we seek to uncover the neural correlates of trust formation, maintenance, and disruption in interactions with AI agents. This groundbreaking research will lay the foundation for enhancing Human-AI teaming.

Key Objectives:

  • Develop novel experimental paradigms to study trust in Human-AI interactions.
  • Acquire and analyse brain signals to identify biomarkers associated with trust.
  • Design and implement machine learning algorithms for real-time trust detection from neural data.
  • Investigate the impact of various factors (e.g., AI performance, user experience) on trust dynamics.
  • Collaborate with experts in neuroscience, artificial intelligence, and human-computer interaction.
  • Disseminate research findings through publications in peer-reviewed journals and presentations at academic conferences, contributing to the advancement of knowledge in relevant fields.

Requirements:

Essential:

  • Bachelor's or Master's degree (2:1 or above) in relevant fields such as Computing Science, Artificial Intelligence, Data Science, Neuroscience, or Neuroergonomics.
  • Strong communication skills.
  • Understanding of the research lifecycle, including hypothesis formulation, method design, prototype development, evaluation, and result interpretation.
  • Experience in data analysis and machine learning.
  • Knowledge of deep learning and/or transformer models.

Desirable:

  • Prior experience in EEG data analysis and modelling.
  • Conducting EEG experiments and user assessments.
  • Analytical aptitude and the ability to work independently.
  • Collaboration skills and proactive mindset.

Funding Information:

  • Full stipend and tuition fee coverage at the home rate for eligible students.
  • Additional funding opportunities for training, networking, and development.

How to Apply:

Interested candidates should email Dr. Yashar Moshfeghi () and include the following attachments:

  • Cover letter detailing contact information, motivation, background, and proposed research question (max 3 pages).
  • Up-to-date CV.
  • Transcripts and certificates of all degrees.
  • Two references, one academic.

Contact Dr. Yashar Moshfeghi to express interest by 31/08/2024. Applications will be processed on a 'first come, first served' basis, and the hiring process will conclude as soon as a suitable candidate is identified.

We are committed to inclusion across race, gender, age, religion, identity, and experience, and we believe that diversity makes us stronger by bringing in new ideas and perspectives. The University of Strathclyde was established in 1796 as “the place of useful learning”. This remains at the forefront of our vision today for Strathclyde to be a leading international technological university that makes a positive difference in the lives of its students, society and the world. Strathclyde was the first institute to win the coveted Times Higher Education “University of the Year” award twice, in 2012 and 2019, and has since been voted the Scottish University of the Year in 2020. 

Biological Sciences (4) Computer Science (8) Information Services (20) Mathematics (25) Psychology (31)

Register your interest for this project


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