University of Technology Sydney (UTS) is located in the heart of Sydney's creative and digital precinct. With state-of-the-art campuses and facilities, we're preparing students to become global thinkers, leaders and innovators.
The Data Science Institute (DSI) is a collaborative impact-focused data science group delivering change for both industry and the wider community. The Institute delivers integrated applied research, linking preliminary data analytics, data engineering and innovative data science. We deliver practical, innovative solutions to solve real-world challenges.
We were among the first Australian institutions to receive the Athena SWAN Bronze Accreditation for our commitment to addressing gender equity in STEMM disciplines. The UTS Women in Engineering & IT program, fosters a network of passionate people who are actively involved in the development of our next generation of young Engineering and IT professionals.
About the role
The UTS Data Science Institute is looking to recruit Postdoctoral Research Fellow / Senior Research Fellow with experience in AI Frameworks for the Time Series Models and Feature Engineering for various crop types. You will develop supervised and unsupervised predictive models and algorithms that support UTS, The Yield's product roadmap and are scalable to cloud architectures.
In this role your core activities will include creating research collaborations, generating high quality publication outputs and developing strong links with professional, government and industry bodies. This position is expected to work very closely with the industry partner.
About you
As a Postdoctoral Research Fellow / Senior Research Fellow, you will have demonstrated project management skills and experience in data mining, data analysis, machine learning, and remote sensing. This role will require you to have data analysis experience pertaining to qualitative and quantitative research designs. You will have strong interpersonal skills and the ability work closely with industry and external academic partners in the delivery of practical impact-focused research in a high-paced delivery environment. To be successful in this role you will have:
• PhD in Computer Science
• Ability to supervise PhD students
• Experience in developing models using machine learning or AI to achieve a business and commercial outcome, preferably in areas such as environmental, climatic, geospatial, marine, or agriculture modeling
Base Salary Range: $114,836 to $135,915 pa (Level B) / $139,598 to $160,317 pa (Level C)
This role attracts 17% superannuation (pension) in addition to the base salary.
This position is full-time and appointment will be made on a 2-year fixed-term basis.
UTS staff also benefit from a wide range of Employee Benefits include flexible work practices, child care centres, generous parental leave and salary packaging opportunities.
Please click the Apply link on the Seek Job Description here.
• full details of the application process;
• a copy of the position statement; and
• information on remuneration, benefits, terms and conditions.
Please use reference number IRC246162 in all correspondence
General enquiries or issues with your application may be directed to Marie Hetherington in the UTS Recruitment Team at recruitment@uts.edu.au.
Closing Date: Thursday, 13th July 2023 at 11.59pm (AEST)
Please note the closing date has been extended from Thursday 29th July - previous applicants need not apply again and applications will be reviewed w/c 17th July)
Applicants must have valid Australian work rights
Accessibility
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In addition, if you have a disability that requires adjustment to the recruitment process or an alternative application pathway please contact recruitment@uts.edu.au for assistance.
UTS is recognised widely as supporting inclusivity and diversity through our Wingara Indigenous Employment Strategy, Social Impact Framework and UTS Access and Inclusion Plan
We welcome applications from women, Indigenous Australians, people with disability, those who identify as LGBTIQ and applicants from culturally and linguistically diverse backgrounds.
