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Round 2

Round 2

Round 1

Round 1

Round 2 Projects


Data Science and Chemistry Applied In-Vivo



VitalTrace is a high growth medical device company founded and based in Perth, and currently developing a world-first biosensor aiming at detecting fetal distress during labour. VitalTrace were recently granted Breakthrough Device Designation by the FDA and have a growing team based in Perth and Melbourne.


VitalTrace was founded in 2017 after the founders met during a Stanford Spark Co-Lab and has been driven by raised Seed, Series A and grant funding. Vitaltrace is seeking to usher in a new age of childbirth monitoring, both minimalist in its application and much safer and reliable than current practice.


The team is a thriving, purpose driven MedTech company, striving to improve the way babies are born. Our mission is to develop cutting edge technology to empower all obstetricians and midwives to deliver the greatest outcomes for mothers and babies during labour.


The project spans two business units; Data Science and Chemistry which will work in parallel to explore the application of science in research & development.


For data science, key project outcomes will be the development of BI Dashboards, after conducting relevant in-vivo trials, exploratory data analysis of the in-vivo trial data, assessing ML models and Data Versioning using baseline algorithms for all in-vivo data in parquet format.


Chemistry will cover enzyme assay development, R&D into new formulations and subsequent analysis of the aforementioned activities.


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Mother Holding Baby Finger

Claire Li

The University of Western Australia

Biomedical Sciences


Skills: Technical skills in molecular biology, bone biology, cell biology, infectious diseases & biochemistry (qPCR, WB, cell culture, IF, etc), image & data analysis (ImageJ, GraphPad Prism), organisational skills, ability to priorities, attention to detail, communication & interpersonal skills, and teamwork.

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Hassan Mahmood

Edith Cowan University

Science, Computing and Security


Skills: Machine/ Deep Learning, computer vision & embedded systems, programming with Python, Pytorch, Tensorflow etc, multidisciplinary and team work.

Data driven mental health and wellbeing support




Oqea is the first person-centric mental health and wellbeing digital platform and consumer app to connect consumers, health providers, businesses, family and friends all in one private, safe and secure online place. Oqea is designed to be preventative in nature and empowers people of all walks of life to make healthy connections with helpful people, information, and tools anytime, anywhere, when they need them.

Sadly, there is a growing need for mental health services, with demand far-exceeding supply. The use of technology to support traditional models is growing and with that, the collection of data. Oqea is a digital platform that augments traditional mental health and wellbeing models by providing a person-centred space for care. As a two-sided marketplace, Oqea has a member side that provides a comprehensive place for self-managed or clinician supported care, and a provider side that offers collaborative multidisciplinary care and communication, as well as practice management.

Working on this project students will:

  • Examine and evaluate current data types, sources, and structures within the Oqea platform

  • Identify ways to utilise data driven insights to provide support recommendations to members

  • Identify optimal ways to evaluate and report wellbeing over time

  • Evaluate/identify the cost benefits of digitally supported care


Anticipated outcomes will be in the form of reports and the analysis of a small subset of data from the platform.

Students will get to utilise their skills and work in multi-disciplinary teams to problem solve and gain experience in the digital health industry.

As a company, we will benefit by having research and domain expertise review aspects of the company and provide valuable insights and data to support our mission to enhance the wellbeing of everyone, everywhere.

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Belinda Neo

Curtin University

Population Health


Skills: Qualitative & quantitative data analysis, project & time management, teamwork & collaboration, communication with stakeholders, adaptable and flexible.


Emma/Asma Emamrezaei

Curtin University

Public Health


Skills: Design & implementation of economic evaluations (such as cost studies, cost-effectiveness modelling), review of the literature, evaluation and synthesis of the finding for systematic and scoping review, manuscript preparation and publishing, time management, decision-making and goal setting.

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Tej Bahadur Shahi


Engineering and Technology


Skills: Machine/Deep Learning (Scikit-learn, Keras, Tensorflow, Weka), programming with Python & R, data visualisation & dashboard (PowerBI, Tableau, Matplotlib, Seaborn, ggplot2), drone-based remote sensing and computer vision.

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Integrating multiple Loop-mediated Isothermal Amplification (LAMP) assays with multiplexed optical detection systems for ultra-high throughput detection of emerging biosecurity threats

Avicena Systems


Avicena is an award-winning Australian MedTech biosecurity company with a ground-breaking, rapid, pandemic-scale surveillance screening instrument to facilitate the detection of pathogens, including COVID-19. Saliva sampling and anonymous ID tracking takes less than 30 seconds. Each Sentinel instrument can process more than 90,000 samples daily with data available in under 30 minutes.


This project required a combined engineering and molecular biologist team.

Engineering aspect of the project entailed developing optical system solutions involving industrial cameras for the detection and analysis of biochemistry assays using fluorometry in innovative ways for scalable commercial application. Whilst first commercial solution has already been developed for against different targets in customised plates, the new research explored techniques for multiplexing using different wavelengths, different optical techniques and different vision processing algorithms to allow multiple pathogen assays to run in parallel. The engineering involved optical design, electronic application, image processing and software processing, typically within a Python development environment.

Molecular biology aspect of the project involved molecular assay design and development, bioinformatics analysis, data collation and analysis, and critical thinking in experimentation to generate compatible assays for multiplex detection with a high sensitivity and specificity.


Close collaboration with the Avicena's Molecular Diagnostics team and Engineering team supported the project throughout whilst building upon established techniques in place today. It was a great opportunity to apply knowledge in the areas of physics, biochemistry and engineering to solve problems with a true commercial application resulting in high impact on health and biosafety.


Ideal candidates had the following skillsets.

Engineering team member:

  • Graduate/ completing postgraduate degree in physics or electronics with skills in scientific research and programming

  • Experience in optics will be advantageous

Molecular Diagnostics team member:

  • Completing a PhD in molecular biology or biochemistry with extensive laboratory experience in molecular assays, including PCR and working within a PC2 environment (eg. RNA purification in biosafety cabinet)

  • Bioinformatics and molecular pathology experience are desirable but not essential


  • 3 months placement

  • In-person engagement

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