Research Fellow in Machine Learning

About the job
Our Data Research Institute is looking for a Research Fellow who has a good understanding of data science and a strong interest in tackling current and emergent challenges that arise in data science relevant areas such as machine learning, data analytics, and scalable data management architectures. You will work as part of a growing multi-disciplinary team of data scientists led by Prof Timos Sellis, Director of the Data Science Research Institute and A/Prof Kai Qin, Leader of the Machine Learning and Intelligent Optimisation Research Group of the Institute while partnering with other parts of Swinburne University of Technology. Along with contributing to the development of the focus area of Machine Learning and working on industry research projects, you will have the chance to build on his/her own existing research record to develop a theme within data science.

As part of Swinburne Data Science Research Institute, you will work with renowned and eminent researchers in developing innovative machine learning techniques and applying off-the-shelf machine learning tools and packages (e.g., PyTorch, TensorFlow, and AWS SageMaker) to resolve versatile data analysis tasks, with a particular focus on analysing various types of image/video data from the fields of remote sensing, health & medicine, transport, etc. You will have access to Swinburne’s supercomputer OzSTAR to conduct research, one of Australia’s most powerful high-performance computing facilities with 230 NVIDIA Tesla P100 GPUs.

About Swinburne
Swinburne is a place where your work can impact the national economy and wellbeing of our society. We promote diversity, support career development, provide flexibility and offer competitive salary packages. Our Data Science Research Institute, in partnership with key industry leaders, is leading the data-to-discovery pathway with its data-intensive research.

About you
To be successful in the role, you will have:

• PhD in data science, computer science, computer engineering or data analysis focused areas within other disciplines such as astrophysics and geophysics, and with demonstrated work experience in machine learning and image/video analysis.

• A strong track record of quality research as evidenced by publications in top-tier journals and conferences

• Prior proven significant research and hands-on experience in machine learning and image/video analysis.

• Previous experience of software development for large projects and manipulating large data collections

A full list of the selection criteria is available within the position description

Benefits

• Onsite health services

• Private health insurance discounts

• Salary package your car parking, superannuation and vehicle lease plans. It can help you get the most value from what you earn.

Discover more discounts when you start at Swinburne. Receive movie tickets and staff membership options at the Swinburne bookshop. Theres news subscriptions and computer and software discounts on offer.

Further information and how to apply
The diverse culture within Swinburne is a source of strength. We are proud to be recognised by the Workplace Gender Equality Agency as an Employer of Choice for Gender Equality 2018 and of key initiatives such as our Pride@Swinburne Strategic Action Plan and our Reconciliation Action Plan which are integral components of our 2025 vision to be world class university creating social and economic impact through science, technology and innovation. Swinburne has also received the Victorian “High Commendation” Multicultural Excellence Award (Business Category).

To view the position description or to start an application click on apply' at the bottom of this page and submit a resume, cover letter and response to the Key Selection Criteria, as listed in the Position Description.

If you are experiencing technical difficulties with your application, please contact the Recruitment team on staffrecruitment@swin.edu.au. 

Should you require further support for an interview due to special needs or consideration, please contact our Diversity Consultant, Dr. Walter Robles, on inclusion@swin.edu.au. For support or queries related to Aboriginal and Torres Strait Islander employment, please contact DeadlyCareers@swin.edu.au.


Applications close at 5pm on Monday 13 May 2019

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