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  Mental Workload Detection from Brain Signals


   Department of Computer and Information Sciences

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Are you intrigued by the complexities of human cognition and its intersection with technology? Are you passionate about leveraging neuroscience to enhance human-computer interaction? We invite applications for a PhD position in the fascinating realm of mental workload detection from brain signals.

Project Overview:

Understanding mental workload— the cognitive demand imposed on an individual during task performance— is crucial for designing efficient and user-friendly systems. This interdisciplinary research project aims to investigate mental workload detection using advanced brain signal analysis techniques. By decoding neural activity patterns, such as electroencephalography (EEG), we aim to develop robust methods for real-time assessment of mental workload levels when interacting with an Information System.

 Key Objectives:

  • Develop experimental paradigms to induce varying levels of mental workload.
  • Acquire and preprocess brain signals using EEG technique.
  • Apply machine learning algorithms to decode mental workload from neural data.
  • Explore the relationship between mental workload and performance in human-computer interaction tasks.
  • Collaborate with experts in neuroscience, psychology, and human-computer interaction to validate findings and translate them into practical applications.
  • 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. 

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

Register your interest for this project


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