CSIRO Industry PhD Scholarship: AI techniques in intelligent manufacturing

Acknowledgement of Country 
CSIRO acknowledges the Traditional Owners of the land, sea and waters, of the area that we live and work on across Australia. We acknowledge their continuing connection to their culture and pay our respects to their Elders past and present. View our vision towards reconciliation.  

The Opportunity

• Gain experience working with industry to solve real-world problems while you earn your PhD
• Develop transferable professional skills
• Get access to specialised expertise, equipment and training

The CSIRO Industry PhD Scholarship (iPhD) Program is an industry-focused, applied research scholarship and training program that brings together an industry partner, the university and CSIRO. 

You will undertake a co-designed research project that will develop your ability to translate research into commercial outcomes.  

You will get real-world experience and access to specialised expertise, equipment and training.

Our graduates develop transferable professional skills and are well positioned to work at the cutting edge of industry focussed research.  

Scholarship details 

The Program includes:

• admission to the university PhD program
• a four-year scholarship package of approx. $45,000 per annum

• a four-year Project Expense and Development package of up to $13,000 per annum
• an in-business component with an industry partner of at least 3 months
• professional development training to enhance your applied research skills
• supervision by CSIRO, the industry partner, and the university

Project Title:  Data-driven robust, explainable AI techniques for process, product quality control, and security in intelligent manufacturing 

Project Description:   Quality, productivity, and security are essential elements in an industrial production plant. Even a slight improvement in productivity, e.g., 1%, can lead to gains of Millions in revenue. These elements are dependent on various factors, including process control and automation. Furthermore, these factors are derived from several probabilistic and deterministic parameters that span from raw material collection and transportation to manufacturing. In this 4-year Ph.D. research project, a student will investigate the novel methods to improve the quality (e.g., customer requirements), productivity (e.g., production time), and security of manufacturing by considering data-driven approaches and leveraging robust explainable artificial intelligence/machine learning algorithms for overall intelligent automation. This project is outcomes-oriented, so the data is collected from the actual plant of Sonac Australia Pty Ltd, an animal feed industry, and the proposed control methods and techniques will be leveraged to optimize the actual plant.  

The project will focus on the process of blood collection and delivery to the production plant. Despite its importance for the quality of the process outcome, this part of the production cycle does not allow for best control and monitoring as it takes place at slaughterhouses and on delivery tankers. The project will look at ways of automating the initial blood processing in the slaughterhouse in a way which prevents damage to the blood (e.g., premature coagulation, impurities, etc.). Also, the process of pumping blood to and from the transporting tanks will be a target for improvement as it has settings (e.g., pumping flowrate) which impact the quality and productivity of the Sonac processes. The outcome of this project will result in better controllability over the pre-plant processing. 

Project supervisors:   

CSIRO
CSIRO supervisor: Chandra Thapa & Seyit Camtepe
Business unit: Data 61
Research program: Software and computational systems
Email address:  Chandra.thapa@data61.csiro.au; and Seyit.Camtepe@data61.csiro.au 

University
University supervisor: Ibrahim Sultan & A/Prof. Feng Xia
Federation University
Email address: i.sultan@federation.edu.au; and f.xia@federation.edu.au
Faculty: Graduate Research School

Industry
Company name: Sonac Australia Pty Ltd
Website: www.sonac.biz  

Location:     

•   Primary location of the student: Federation University, Ballarat, VIC
•   Other potential locations: CSIRO Data 61 (Melbourne or Sydney Site)
•   In-business component with Industry partner: Sonac production plant – 281 Maryborough Dunolly Rd Havelock, Vic 3465

Overview of who we are looking for 

The ideal student will have:

• Strong background in process engineering, mathematics, including probability and statistics, programming experience in python, knowledge of ML/AI algorithms and frameworks, and understanding of industrial control systems and SCADA; and
• Good communications skills and the ability to interact with all levels.

Eligibility

To be eligible to apply you must:

•   be an Australian citizen or permanent resident, or a New Zealand citizen
•   meet host university PhD admission requirements
•   meet university English language requirements
•   not have previously completed a PhD
•   be able to commence the program in the year of the offer
•   enrol as a full-time PhD student

Applications will be assessed on:

Experience relevant to the field of research, including any research experience

Suitability for the project

Academic excellence

Motivation for undertaking an Industry PhD project

About CSIRO
At CSIRO Australia’s national science agency, we solve the greatest challenges through innovative science and technology. We put the safety and wellbeing of our people above all else and earn trust everywhere because we only deal in facts. We collaborate widely and generously and deliver solutions with real impact.  

Further information about the CSIRO Industry PhD Program can be found at:  www.csiro.au/iphd   

How to Apply:

Applicants should contact Associate Professor Ibrahim Sultan, at i.sultan@federation.edu.au prior to submitting an application.

For details on how to apply, please see the Federation University website at CSIRO Industry PhD Program Scholarship – Federation University Australia  

Applications close:   25 November 2022, 11:00pm AEDT