Vonwiller Research Associate / Fellow
- Full time, fixed term for 2 years
- Opportunity to work at the forefront of coronary heart disease research
- Base Salary, Level A/B $97,043 p.a. - $134,403 p.a + 17% superannuation
About the opportunity
Are you excited by the opportunity to apply machine learning techniques to discover new solutions to human health?
Early detection and treatment can prevent the progression of coronary artery disease (CAD) and, consequently, heart attacks. While this can help individuals who display traditional risk factors such as diabetes, hypertension, high cholesterol, and smoking, many people develop CAD over years without the presence of any obvious risk factors. They remain unaware of their susceptibility to the disease and miss out on the opportunity to reduce their risk of a heart attack through taking lifesaving drugs.
CAD Frontiers is an Australian-led, global team composed of clinicians, researchers, data scientists, healthcare and industry leaders with a track record of discovery, innovation and translation. CAD Frontiers is partnering with the Digital Sciences Initiative (DSI) at the University of Sydney to explore the convergence of digital sciences in information, algorithms and machine learning for enhancing the impact and success of diagnostic intervention. By partnering with DSI, CAD Frontiers will build capacity to achieve rapid and demonstrable outcomes in research and commercialisation. The Digital health imaging team within DSI will support CAD Frontiers to improve the understanding, diagnosis and treatment of subclinical disease through developing multimodal AI algorithms that incorporate multiple data sources. AI algorithms for cardiac imaging data, co-designed with multidisciplinary domain expertise, can aid in image understanding and in extracting 'deep' image feature for 'image-omics' - an approach that associates imaging features with complementary -omics data for new biomarker discoveries. This work will revolutionise the clinical approach to early diagnosis of CAD through the discovery of novel biomarkers and the more efficient and affordable analysis of diagnostic imaging data. DSI's established dynamic digital business ecosystem is expected to provide CAD Frontiers with an important interface with start-ups through to multinational industry partners during the commercialisation phase. The partnership aims to maximise industry investment, competitiveness and the likelihood of delivering economic and health outcomes.
We have secured funding through the Vonwiller Foundation to support a Vonwiller researcher to develop novel clinical and data science approaches to CAD diagnostics. Working collaboratively, the researcher will accelerate research in applied machine learning to ultimately identify the molecular biosignatures of patients with silent atherosclerosis, and the application of these AI algorithms to imaging held in data banks such as BioHeart. Working in an interdisciplinary manner will bring together medical, computer science and engineering mindsets to apply a smart digital solution to a devastating physical problem.
The appointment will be at Level A or B dependent on experience.
The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the importance of recruiting talent aligned to these values and are looking for a Researcher who has either one of the following skillsets:
- tertiary qualifications in Medical Informatics, Computer Science, Machine Learning / Deep Learning / AI (or near completion)
- skills in software development including work with Python, C/C++ and the latest machine learning packages
- prior experience working with medical imaging modalities, in particular coronary artery disease imagery and related biomarker data, is desirable
- tertiary qualifications in Data Science, Bioinformatics, Computer Science, applied machine learning or similar (or near completion)
- skills in applied machine learning development with medical imaging data using R or Python packages
- prior experience on recent platforms of omics data such as next generation sequencing or mass spectrometry is desirable
- high-dimension data analysis experience is desirable
We are looking for the following from all candidates:
- demonstrated skills and experience necessary to manage the processes for testing and validation of machine learning algorithms in a clinical environment
- demonstrated ability to conduct research / scholarly activities as part of a multidisciplinary research team
- experience managing large volumes of multi-modality data and a demonstrated track record of supporting high quality academic publications and clinical uptake
- the ability to liaise effectively with both scientific/technical and clinical colleagues
- ability to assist researchers from other disciplines as well as working with PhD students
To keep our community safe, please be aware of our COVID safety precautions which form our conditions of entry for all staff, students and visitors coming to campus.
Your employment is conditional upon the completion of all role required pre-employment or background checks in terms satisfactory to the University. Similarly, your ongoing employment is conditional upon the satisfactory maintenance of all relevant clearances and background check requirements. If you do not meet these conditions, the University may take any necessary step, including the termination of your employment.
At the University of Sydney, our shared values include diversity and inclusion and we strive to be a place where everyone can thrive. We are committed to creating a University community which reflects the wider community that we serve. We deliver on this commitment through our people and culture programs, as well as key strategies to increase participation and support the careers of Aboriginal and Torres Strait Islander People, women, people living with a disability, people from culturally and linguistically diverse backgrounds, and those who identify as LGBTIQ. We welcome applications from candidates from all backgrounds.
How to apply
Applications (including a cover letter, CV, and any additional supporting documentation) can be submitted via the Apply button at the top of the page.
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For a confidential discussion about the role, or if you require reasonable adjustment or support filling out this application, please contact Linden Joseph or Rebecca Astar, Recruitment Operations, Human Resources on email@example.com
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The University reserves the right not to proceed with any appointment.
Click to view the Position Description for this role.
Thursday 08 June 2023 11:59 PM
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