PhD Stipend Scholarship – School of Primary and Allied Health Care, Monash University [Moving beyond wearables - Enhancing care and safety of hospitalised older adults using advanced localisation/motion-based sensor technologies]
Job No.: 693752
Location: Peninsula campus
Employment Type: Full-time
Duration: 3.5 years fixed-term appointment
Supervisory Team: Associate Professor Christina Ekegren (Main Supervisor)
The successful candidate will be supported by a multidisciplinary project team with expertise in ageing, healthcare, biostatistics and digital health. Refer to www.research.monash.edu/en/persons/christina-ekegren (for main supervisor profile). Other supervisors to be confirmed.
Remuneration: A PhD annual stipend for 3.5 years of full-time PhD enrolment ($37,145 per annum for 2026 rate). The stipend rate is indexed annually and published on the Monash University Graduate Research Stipend and Allowance Rates website
Additional support also available for project resources and conference presentation:
- School of Primary and Allied Health Care HDR Support Fund of up to $4,000 for the duration of candidature
- Monash Graduate Research Travel Grant
- The successful PhD candidate will participate in collaborative activities of the project team relating to this project funded by the National Health and Medical Research Council (NHMRC). This collaboration will further assist in consolidating the PhD candidate’s skills and capabilities.
The Opportunity:
The PhD stipend scholarship offers an outstanding opportunity for talented candidates to undertake PhD study in the field of care and safety of hospitalised older adults using advanced localisation/motion-based sensor technologies.
The PhD Project:
This PhD forms part of an NHMRC Ideas Grant (‘Smart Ward PREDICT’), based at the Rehabilitation, Ageing and Independent Living (RAIL) Research Centre, School of Primary and Allied Health Care, Monash University, using data from the National Centre for Healthy Ageing Smart Ward and Healthy Ageing Data Platform.
New localisation and motion-based sensor technologies, including radar, RFID and Bluetooth, are being rapidly developed and deployed in aged care, home and hospital settings. However, there is a lack of real-world evidence on their acceptability, validity and clinical relevance, potentially leading to wasteful health and aged care spending.
The aim of this project is to build the evidence base on new localisation and motion-based sensor technologies in hospital settings in order to guide clinical policy and practice.
Specific research questions may include:
- What is the current published evidence on the psychometric properties and impact of localisation/motion-based sensor technologies in health, aged and home care settings?
- What are the psychometric properties of these technologies for measuring movement and falls in hospitalised older adults? – algorithm development and technical validation
- Can movement patterns detected using these technologies help predict adverse outcomes in hospitalised older adults, e.g. delirium, behaviours of concern, using machine learning approaches? – clinical validation
- What are the perspectives of patients and staff on the acceptability, feasibility and clinical utility of localisation and motion-based sensor technologies in hospital settings?
Selection criteria:
Prospective applicants must satisfy Monash University PhD entry requirements as outlined in the Monash Graduate Research Admission Procedure, including, but not limited to:
- An Australian citizen; or New Zealand citizen; or have been granted permanent resident status; and
- A bachelor’s degree of at least four years in a relevant discipline, which includes a research thesis or project, with a minimum overall average grade of an Honours degree equivalent to the First Class Honours Division A; or
- A master's degree in a relevant discipline which includes a research thesis or project equivalent to at least 25 percent of one year of full-time study, with a minimum overall average grade of honours equivalent to the First Class Honours Division A; or
- A qualification, or combination of qualifications and relevant professional or research experience, deemed equivalent by the Graduate Research Committee (or delegate); and
- Preferably, be available to commence as a full-time PhD candidate in Q4 2026 (or by arrangement).
- Preferably, background or experience in sensor technology, psychometrics, and a relevant clinical discipline such as nursing, medicine or allied health.
How to apply
We are seeking expressions of interest from talented candidates who wish to apply for the PhD and contribute to this exciting project. This position has a two-stage selection process:
Stage 1: Submit an Expression of Interest (EOI) to Dr Michelle Shannon, michelle.shannon@monash.edu
For general instructions on how to apply for roles at Monash, please refer to How to apply for Monash jobs. Candidates should include the following when submitting an EoI:
- A cover letter not exceeding 500 words that includes a brief statement of suitability and why you are interested in pursuing a PhD in this research project/area.
- A curriculum vitae, including a list of any peer-reviewed publications, conference presentations and relevant work and/or research experience.
- Scanned copies of academic transcripts.
- Contact details of two academic and/or clinical referees, at least one of whom must be an academic referee.
Stage 2: Submit a PhD candidature application
For the candidate whose EoI in Stage 1 is successful will be invited to submit a full application for Monash PhD candidature.
Enquiries: Dr Michelle Shannon, michelle.shannon@monash.edu
Applications Close: Sunday 28 June 2026, 11:55pm AEST
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