Dr. Ayse Zengin

Associate Professor

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Biography

Ayse Zengin is an Associate Professor in the Department of Medicine at Monash University, where she leads the Bone Muscle Research Group. She holds a PhD from the Garvan Institute of Medical Research and has a background in bone biology and neuroscience. After completing her doctoral studies in 2012, she worked on a postdoctoral fellowship in the Department of Endocrinology at Ludwig Maximilians University in Munich. Her research has focused on ethnic differences in bone health, particularly among underserved populations, including Aboriginal Australians and communities in Gambia and India. She utilizes advanced imaging techniques and artificial intelligence in her research to improve the management of musculoskeletal health. In recognition of her contributions, she received the Dean’s Award for Research Excellence from Monash University in 2021. Ayse is actively involved in mentoring early-career researchers and contributes to multiple professional organizations such as the American Society for Bone and Mineral Research.

Research Interests

Experience

Postdoctoral Fellow

2012-09-01 — 2013-12-31

Ludwig Maximilians University • Munich, Germany

Conducted research in the Endocrinology Department focusing on bone metabolism.

Investigator Scientist

2014-01-14 — 2016-08-31

MRC Human Nutrition Research • Cambridge, UK

Led projects studying musculoskeletal health in ageing populations.

Awards

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Dean’s Award for Research Excellence

2021-01-01

Requirements for Monash University

Master Program
Requirements
GPA Requirement
Required:3
IELTS
Listening
Required:6
Reading
Required:6
Writing
Required:6
Speaking
Required:6
Overall
Required:6.5
TOEFL
Listening
Required:12
Reading
Required:13
Writing
Required:21
Speaking
Required:18
Total
Required:79
Prerequisites
Bachelor degree (or equivalent) in a related field
Application Checklist
  • Academic Transcripts
  • Proof of English Proficiency
  • Curriculum Vitae
  • Copy of Passport
Specialization Notes

Requirements are standardized across the Faculty of Information Technology for most Master's programs including Computer Science and Data Science.