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AI/Machine Learning Senior Research Associate – SBC

Apply nowJob no: 499753
Work type: Full-time
Location: Sydney, NSW
Categories: Post doctoral research fellow

  • Full time (35 hours/week), 3 year fixed term contract
  • Level B: $108K-$126K +17% superannuation + leave loading
  • Liverpool Hospital
OVERVIEW OF RELEVANT AREA AND POSITION SUMMARY
UNSW Medicine is a national leader in learning, teaching and research, with close affiliations to a number of Australia’s finest hospitals, research institutes and health care organisations. With a strong presence at UNSW Kensington campus, the faculty have staff and students in teaching hospitals in Sydney as well as regional and rural areas of NSW including Albury/Wodonga, Wagga Wagga, Coffs Harbour and Port Macquarie.
The South Western Sydney (SWS) Clinical School is based at Liverpool and Bankstown-Lidcombe Hospitals, Sydney. The School is the fastest growing School within UNSW Medicine and is renowned for its excellence in undergraduate teaching and clinical training as well as in the range of research areas such as Cancer, Clinical Science, Community and Population Health, Early Years/Childhood Health, Injury and Rehabilitation and Mental Health.
We are establishing a Stroke and Neuroscience research team based at South Western Sydney Clinical School. The research team will work on AI/machine learning methods on brain imaging data (for example, detection of acute ischemia).
The Senior Research Associate (AI/Machine Learning) will be involved in developing new machine learning algorithms (CT and MRI). The role of Senior Research Associate (AI/Machine Learning) will work closely with senior researchers and provide support across several ongoing projects in various areas of the research. The Senior Research Associate (AI/Machine Learning) will drive a research project in the SBC, experimentally support other students and researchers in the group, help define and drive new research directions aligned with the overall goals of the group and the Centre.
The role of Senior Research Associate (AI/Machine Learning) reports to the Professor of Medicine & Brain Imaging Laboratory Manager and has no of direct reports.
RESPONSIBILITIES
• Play a major role in all aspects of major research projects including management and/or leadership of a research team
• Lead the preparation, research analysis, and produce/contribute to conference abstracts and publications for submission to peer-reviewed journals
• Publish and present research results at academic/industry national and international conferences
• Provide scholarly supervision and mentor undergraduate, honours level and post graduate research students and actively provide guidance on research methods
• Design, develop and lead research ensuring all research is conducted to methodological and ethical standards
• Formulate study design, programmes, and implementation timelines
• Collaborate effectively and maintain strong relationships with relevant researchers within the institution, stakeholders and policy makers and contribute to the teaching program within the field of research expertise
• Develop proposals for national competitive research grant funding
• Complete administrative functions primarily connected to area of research
• Cooperate with all health and safety policies and procedures of the university and take all reasonable care to ensure that your actions or omissions do not impact on the health and safety of yourself or others.
SELECTION CRITERIA
• A degree in Science, Engineering, Computer Science or equivalent with relevant experience; or extensive
experience and management expertise; or an equivalent combination of relevant experience and/or
education/training.
• Proven track record in publishing scientific research papers in immunology and demonstrated experience
in grant writing and ethics submission
• Excellent oral and written communication skills, attention to detail and the ability to liaise effectively with
all levels of staff, students, management, collaborators, and members of the public with relevant data
analysis skills
• Proven ability in working effectively as a member of a multidisciplinary team and in supervising technical
   staff and students
• Demonstrated experience in relevant industry and/or in establishing/maintaining research partnerships
   with industry.
• Experience with medical image processing and annotation
• Experience managing large volume of DICOM data and familiarity with PACS
• Programming experience (any language) and an interest in developing and applying programming skills
• Ability to participate in production of research publications
• Ability to work autonomously with considerable independence
• Familiarity with clinical trials and patient confidentiality rules and regulations
• Experience handling clinical trial data and preparation of data for analysis by external parties
• Previous experience working in a multidisciplinary team environment in a technical capacity
• Strong written and verbal communication skills for managing external site relations
• Strong organisational and time management skills, including the ability to prioritise workloads, work well
  under pressure, and organise own work and others to meet deadlines
• Demonstrated ability to be flexible and adaptable in a changing environment
• Excellent stakeholder engagement skills, including managing competing demands and stakeholder groups.

To Apply:   Please submit your resume and cover letter to be considered for the opportunity.

Position Description: Download File 00085396 – Level B – Senior Research Associate – Parsons Group (AI) (2).pdf

 

Contact:

Corrie Buchanan

Talent Acquisition Consultant

e: corrie.buchanan@unsw.edu.au (please apply direct on the portal only, applications sent direct to email will not be accpeted)

Applications close: 11:00pm, Sunday the 16th of August 

UNSW aspires to be the exemplar Australian university and employer of choice for people from diverse backgrounds. UNSW aims to ensure equality in recruitment, development, retention and promotion of staff and that no-one is disadvantaged on the basis of their gender, cultural background, disability, sexual orientation or identity or Indigenous heritage. We encourage everyone who meets the selection criteria to apply.

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