This project aims to advance the field of human movement science by addressing the challenges encountered when developing a low-cost, automated system for screening the movement of pre-elite student-athletes. Leveraging state-of-the-art artificial intelligence (AI), markerless motion capture and stereo vision technologies, this research will tackle critical challenges in biomechanics and sports science.The overarching vision is that the developed system will integrate sport-specific performance metrics with biomechanical analyses, enabling deeper insights into motor function and movement strategies. This innovative approach has the potential to revolutionize athlete assessment, providing actionable feedback for injury prevention, performance enhancement, and tailored training programs.By contributing to the fields of AI, biomechanics, and applied sports science, this project offers a unique opportunity for PhD candidates to engage in interdisciplinary research with real-world impact.The project also includes a three-month industry engagement component with the industry partner (Toowoomba Grammar School).ValueStipend of AUD $47,000Maximum period of tenure of an award is 4 years. Periods of study already undertaken towards the degree will be deducted from the period of tenure.A four-year Project Expense and Development package of $13,000 per annum to cover project operating expenses and development activities. Project expenses may include lab consumables, fieldwork, attendance at top-tier conferences and other research costs.A three-month industry engagement component with the industry partner.A structured professional development and training program to develop your applied research skills.Eligibilitynot be receiving equivalent support providing a benefit greater than 75% of the students stipend rate;Be an Australian citizen or Permanent Resident, or a New Zealand citizen.Meet university English language requirements.Not have previously completed a PhD.Be able to commence the Program in the year of the offer.Enrol as a full-time PhD student. Part-time arrangements may be considered if approved by the supervisory team and in accordance with university policy.Be prepared to be located at the project location(s) that the host university has approved and, if required, comply with the host universitys external enrolment procedures.Be prepared to undergo onboarding to CSIRO, which will include passing mandatory government background checks (allow for between 4 to 8 weeks) and complete any other CSIRO requirements.Selection criteriaTo be eligible applicants must:Have a basic understanding of machine learning algorithms and deep learning models.Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch.Have strong linear algebra and computer vision knowledge.Have demonstrated responsibility, perseverance, and commitment to achieving project goals and meeting deadlines.Desirable Skills:Experience with biomechanical analysis; sports science research or athlete performance assessment.Familiarity with stereo camera systems.Ability to work collaboratively in an interdisciplinary team.Strong communication skills.How to applyTo apply, please submit an Expression of Interest (EOI). The EOI should include the following:Curriculum vitae; encompassing any research presentations and/or publications. Citizenship / permanent residency status should also be listed here.Education qualifications (testamur and academic transcripts for all undergraduate and postgraduate awards)A one-page (maximum) cover letter, detailing why you would like to undertake this project and why you are suitable for this project with reference to the topic area and selection criteria listed.EOIs must be submitted via the UniSQ Scholarship Application Management System.If you require assistance in completing your application please download Scholarships Online Application Manual (PDF 2.14MB).Closing dateOpen until the scholarship is filled.Further informationFurther information about this scholarship can be obtained from Dr. Ben Hoffman by emailing ben.hoffman@unisq.edu.au. #J-18808-Ljbffr
Job Title
AI-driven markerless motion capture system for enhancing human motor function sc