Study level

  • PhD

Faculty/School

Faculty of Engineering

School of Civil and Environmental Engineering

Topic status

We're looking for students to study this topic.

Research centre

Centre for Data Science

Centre for Future Mobility/CARRSQ

Supervisors

Professor Ashish Bhaskar
Position
Professor in Civil Engineering
Division / Faculty
Faculty of Engineering
Professor Ronald Schroeter
Position
Seeing Machines Chair
Division / Faculty
Faculty of Health

Overview

The project aims to the explore concept of empathic machines in the context of driver monitoring systems (DMS) and automated driving. The successful candidate will contribute to advancing the understanding of driver engagement, situation awareness, and risk through leveraging advancements in data science techniques on vehicle sensor, DMS, and other related datasets.

To apply for this position, please submit the following documents:

  • a cover letter outlining your research interests, relevant qualifications, and motivation to join the Empathic Machines project
  • a detailed curriculum vitae (CV) highlighting your academic achievements, research experience, and any publications
  • academic transcripts from previous degrees
  • samples of your previous data science work, including code repositories or links to relevant projects, if available
  • contact information for three professional references.

Submit your application via email to ashish.bhaskar@qut.edu.au.

Research activities

Candidate with work closely with industry on this industry sponsored project.

Outcomes

The outcome of this research is expected to be used by the industry partners and will have a direct impact on the state of the art and practice in DMS.

Skills and experience

  • A masters degree (or equivalent) in data science, computer science, statistics, or a related discipline.
  • Strong background in data science, machine learning, statistical analysis, or a relevant field.
  • Proficiency in programming languages such as Python, R, or similar, for data manipulation, analysis, and modeling.
  • Familiarity with data management tools and techniques for handling large-scale datasets.
  • Experience in working with sensor data or time-series data is desirable.
  • Solid understanding of machine learning algorithms, feature engineering, and model evaluation techniques.
  • Strong analytical and problem-solving skills, with the ability to derive meaningful insights from complex datasets.
  • Excellent communication skills, both written and verbal, for presenting research findings and collaborating with interdisciplinary teams.
  • Ability to work independently and as part of a team, managing multiple tasks and priorities effectively.
  • Demonstrated publication record (or potential) in peer-reviewed conferences or journals is advantageous.

Scholarships

You may be eligible to apply for a research scholarship.

Explore our research scholarships

Keywords

Contact

Contact the supervisor for more information.