Civil and Structural Engineering PhD Scholarship
Curtin University
Description/Applicant information The candidates will be working in the area of structural health monitoring of civil engineering structures on an Australian Research Council Future Fellowship project “Innovative Data Driven Techniques for Structural Condition Monitoring”. Research topics on vision based techniques for vibration measurements, artificial intelligence techniques, data analysis and signal processing techniques for structural health monitoring are covered in this project.
Student type
- Future Students
Faculty
- Faculty of Science & Engineering
- Engineering courses
Value
This scholarship provides initially $28,092 stipend per annum, based on full-time studies, up to a maximum of three and a half years. For international students, tuition fees for the duration of the award could be covered.
Maximum number awarded 2
Eligible courses
Doctor of Philosophy - Civil Engineering,
(possibly) Doctor of Philosophy - Computing
https://study.curtin.edu.au/offering/course-research-doctor-of-philosophy---civil-engineering--dr-cvengrv1/
https://study.curtin.edu.au/offering/course-research-doctor-of-philosophy---computing--dr-comptgv1/
Eligibility criteria
- Full time enrolment, for both domestic and international students
- Minimum required: Bachelor degree (the first class honours or upper second class honours) in Civil Engineering, Structural Engineering or related fields.
- The language requirement (IELTS: Overall 6.5, Speaking, Writing, Reading and Listening 6.0; or TOEFL, internet based Overall 79, Reading 13, Listening 13, Speaking 18 and Writing 21) is provided at https://study.curtin.edu.au/applying/english-language-requirements/accepted-english-proficiency-tests/. Other general admission requirements and procedures can be checked at http://futurestudents.curtin.edu.au/research/apply/.
- Applicants with Master degrees by research with technical publications and research experiences in structural dynamics and structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data analysis techniques, are preferred.