CIRES PhD, Knowledge mining

About the Role
This research is a unique opportunity to develop skills and experience in advanced artificial intelligence technologies and work closely with IP Australia to gain insights into latest scientific discoveries and innovations.

About the Scholarship
This scholarship is offered by an innovative HDR training program with the Centre for Information Resilience (CIRES). The Centre aims at building workforce capacity in Australian organisations to create, protect and sustain agile data pipelines which are capable of detecting and responding to failures and risks across the information value chain in which the data is sourced, shared, transformed, analysed, and consumed. This research is funded in partnership with IP Australia, with the aim to create a pathway for automated knowledge mining from scientific articles and invention patents.

Many pre-existing technologies can be identified via patent documents and academic research papers, a process that is sometimes referred to as ‘horizon scanning’ or ‘technology landscaping’. This project aims to develop a novel AI-driven approach to technology landscaping based on patents and academics. The new approach will support the strategic positioning of firms and research groups by enabling an insight into the landscape of pre-existing technologies relevant to their proposed innovations. This research will provide insight into areas where gaps in the current technology landscape exist, thus identifying promising future areas of potential innovation. This project will involve the application of natural language processing and machine learning algorithms to identify patents and research papers that are semantically similar to a given query document, as well as topic modelling to identify technology gaps.

This PhD scholarship is based at Swinburne University of Technology, Melbourne, and will also include a placement with government partner organisation IP Australia. It may also involve some travel to meet with CIRES partner at the University of Queensland, Brisbane.

It is advantageous, but not essential, that applicants have:

• a background in Data Science, Computer Science, Machine Learning, or similar science and technology background
• a strong interest in technology and the business value of advanced data analytics, AI and machine learning

Essential Eligibility Criteria
Please respond to this selection criteria as part of your application.

• Meet the entry requirements for Higher Degrees by Research at Swinburne University of Technology
• Understanding of Natural Language Processing and data mining
• Experience working with big data and large scale databases
• Demonstrated strong social and adaptive communication skills to interact with a range of people from the community, industry and university sector
• Ability to contribute to peer-reviewed academic publications
• Must be in a position to enrol and commence the PhD candidature by end of August 2022.

About Swinburne University of Technology
Swinburne Horizon 2025 draws upon our understanding of future challenges. With this new strategic plan, we choose to build Swinburne as the prototype of a new and different university – one that is truly of Technology, of Innovation and of Entrepreneurship, and proud of it. We are committed to a differentiated university proposition in education and research, so that:

• Every Swinburne learner gets a work experience
• Every Swinburne graduate gets a job
• Every Swinburne partner gets a tech solution
• Swinburne is the prototype of global best practice

The achievement of our 2025 moon shots depends on our capacity to work collectively, always, as One Swinburne.

To Apply
To view the position description or to start an application, click on 'apply' or ‘begin’ and submit a resume, cover letter, and responses to the Essential Eligibility Criteria as listed in the position description.

For further information about this role and to review the position description; please click 'apply' or click onto it at the bottom of the advert.

This scholarship will be governed by the Swinburne Research Scholarship Terms and Conditions.

All scholarship specific queries can be directed to A/Prof Amir Aryani (CIRES) at aaryani@swin.edu.au

Please Note: Appointment to this position is subject to passing a Working with Children Check.

If you are experiencing technical difficulties with your application, please contact the Swinburne Talent Acquisition Team on staffrecruitment@swin.edu.au


Applications close at 5PM AEST Friday 8 July 2022

Swinburne offers flexible working options, leave and parenting/carer policies to support work life balance.

Diversity and Inclusion
Swinburne is a large and culturally diverse organisation and we are proud of our commitment to equity and inclusion through key initiatives. For further information on all our initiatives visit our equity and diversity website

We welcome and encourage applicants from diverse backgrounds to apply.

We are committed to making the recruitment process fair and equitable for all our candidates. If you have specific accessibility or support requirements please contact Maree Norden, Diversity & Inclusion Manager at inclusion@swin.edu.au

Aboriginal and Torres Strait Islander
We welcome and strongly encourage applications from Aboriginal and Torres Strait Islander people.

For any support please contact our Aboriginal Employment Coordinator at DeadlyCareers@swin.edu.au or for more information on our Indigenous strategies please follow the link to our RAP Reconciliation Action Plan

Vaccination Requirements
Orders from Victoria’s Minister for Health require all Swinburne workers to be fully vaccinated against COVID-19, unless they are an Excepted Person as defined by the Pandemic COVID-19 Mandatory Vaccination (Specified Workers) 2021 Order (No. 1). All applicants must therefore be able to comply with this requirement.

We are a 2022 Circle Back Initiative Employer – we commit to respond to every applicant.