Research Fellow at Griffith University - ScoutJobs - The AI-curated global job board
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Griffith University
Posted 12 hours ago

Research Fellow

Griffith UniversityResearch Fellow

Perks & benefits

Health InsurancePaid Leave

Requirements

PhD in musculoskeletal biomechanics, Expertise in computational biomechanics, Experience in human biomechanics data collection, Track record in research publications

Skills

Clinical ResearchBiomechanics

About the role

Responsibilities

  • Lead biomechanics data collection, processing, and analysis for clinical research projects
  • Conduct high-quality research to advance the objectives of the Australian Centre for Precision Health and Technology (PRECISE)
  • Publish research findings in peer-reviewed journals and support grant funding applications
  • Prepare research publications, technical reports, and professional presentations
  • Support the supervision and mentoring of research students and junior staff members

Requirements

  • PhD in musculoskeletal biomechanics or a closely related discipline
  • Demonstrated expertise in computational biomechanics, specifically regarding the lower extremity
  • Experience in collecting and analysing human biomechanics data, ideally within clinical populations
  • A strong track record or emerging potential in research publications and securing grant funding
  • Excellent collaboration and communication skills for working within multidisciplinary teams

Benefits

  • Competitive salary with 17% superannuation
  • Salary packaging options and corporate health discounts
  • Generous leave entitlements, including paid parental leave and leave loading
  • Access to health, safety, and wellbeing initiatives, including on-campus gym facilities

About the Company

Griffith University is a leading research institution dedicated to academic excellence and the transformative power of education. Through the Australian Centre for Precision Health and Technology (PRECISE), we are advancing healthcare by moving beyond one-size-fits-all treatments toward personalised care using digital twin modelling platforms.

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