PhD (Industry) scholarships

PhD (Industry) – A collaboration between IBM, Victorian Government and Federation University, supported by funding from Destination Australia Program

Federation University is seeking ten new PhD candidates for fully funded projects co-designed and co-supervised by IBM scientists and leading academics within the University’s newly established research centres.

The PhD candidates will be based at Federation University’s Mt Helen Campus in Ballarat and will receive a scholarship of $30,000 per calendar year in partnership with IBM Watson. Australian citizens and permanent residents will receive a fee offset scholarship and international applicants may be eligible for a tuition fee scholarship.

These opportunities are open to suitably qualified people with relevant academic qualifications and demonstrated fundamental technical knowledge (e.g. multimedia processing and analytics, machine learning, IoT and data science) relevant to the application domain of each project (e.g. health, engineering and supply chain). Specific skills and interests relevant for each project are listed below.

Applications from Australian citizens, permanent residents, and international residents with appropriate qualifications, skills and interests will be ranked highly.

This program will be funded through grants provided by the Victorian Government, the Destination Australia Scholarship program, and IBM.

Successful applicants may have the opportunity to participate in an industry-based internship.

Scholarship details

Scholarship amount: $30,000 per annum*, funded by Department of Education and Destination Australia

Fees: Up to $32,000 per annum covered by a Research Training Program Fee-Offset Scholarships (domestic students) or a Federation University Tuition Fee Scholarship (international students)

Applications have now reopened - Please submit your application and EOI by Sunday 1 May 2022.

Eligibility

Scholarship applicants must be eligible to undertake a PhD. Please verify that you meet eligibility requirements outlined on the Graduate Research School website before you apply. If you are making a case for ‘Honours equivalence', in order to demonstrate your eligibility, please ensure that you provide detailed information to support your case.

Applications are open to Australian residents, permanent residents and international applicants.

Conditions

Students are expected to commit to full-time study for the duration of their candidature.
Scholarships are for a period of three years and extension to scholarships will not be granted.
Successful scholarship applicants must reside in or relocate to Ballarat and study on campus.
Successful scholarship applicants must participate in an internship period.
Successful scholarship applicants must formally assign, in advance, all right, title and interest they may have in any IP developed to the University prior to commencing a project.

View the general conditions for Federation University HDR Scholarships on the Graduate Research School website. Where these conditions differ to those on this form, the conditions outlined for this specific scholarship take precedence.

How to apply

Applicants who do not complete all steps will not be considered.

Step 1: Follow the application process outlined at: How to apply

Step 2: Provide a 1000-word statement covering the following areas:

Discuss your motivations for applying for this PhD scholarship, and your intended outcomes (both for yourself (personally and professionally), and for the sector)
Discuss some of the key existing research literature which impact this topic area
Discuss your relevant background and experience as they are relevant to the project
Discuss how an internship may positively impact on your project and future career.
Discuss potential challenges and how you might overcome them

Applicants are not required to provide a 250 Project Summary.

Step 3: Complete the Scholarship Expression of Interest Form

Projects

Please direct any questions regarding the projects to the first listed supervisor in the project description.

1. Developing digital approaches to chronic disease management in older people

Description

The Happy Life Club is a global innovative clinician coach driven method to assist patients with chronic illness management. Chronic illnesses account for 90 per cent of global disease burden. The PhD project will employ digital health approaches, AI and multimodal data analytics to maximise patient reach and clinical effectiveness.

Background and interests relevant to the project

IT, data science, behavioural sciences

Supervisors

Federation University: Professor Colette Browning, Professor Shane Thomas
IBM: Dr Rahil Garnavi, Dr Michal Chorev

Affiliated research centre

Health Innovation and Transformation Centre

2. Artificial intelligence and health crisis support

Description

Traditionally, the crisis support of the health care is via phone, but more recently support is being provided via online chat and SMS text. This research seeks to significantly boost this capacity to rapidly meet the mental health impacts of future crises (COVID 19), through the development of novel artificial intelligence (AI) and machine learning (ML) solutions.

Skills and interests relevant to this project

IT, data science, AI, Engineering

Supervisors

Federation University: Associate Professor Madhu Chetty, Professor Britt Klein, Associate Professor Peter Vamplew

IBM: Dr Jonathan Vlahos

Affiliated research centre

Health Innovation and Transformation Centre

3. Applying artificial intelligence and machine learning for repurposing drugs

Description

Left untreated, Chronic Kidney Disease (CKD) could result in several complications and even death. Randomised controlled trial used for evaluating the effectiveness of a drug is often costly and lengthy. Utilising available datasets (noisy and with biases), the proposed research would employ artificial intelligence, machine learning and causal inference methodologies to assess drug effects for improved outcomes.

Academic background

IT, data science, AI, bioinformatics and/or computational biology

Supervisors

Federation University: Professor Fadi Charchar, Associate Professor Madhu Chetty

IBM: Dr Michal Chorev

Affiliated research centre

Health Innovation and Transformation Centre

4. Decision support to achieve net zero carbon for land managers and enterprise managers

Description

To meet sustainable development goals, many enterprises aspire to achieve net zero carbon. Working with regional industries, this project uses sensors and artificial intelligence to access the quantity and quality of data for carbon monitoring, modelling, accounting and visualisations, as an evidential base for their claims of carbon neutrality.

Project outcomes will include collaborative development, regional data sharing and the uptake of digital technologies including data federation, visualisation, image analysis, modelling and AI.

Additional support: This project is also supported by the Food Agility CRC with a $10,000 per annum top-up scholarship for a period of three years.

Academic background

IT, data science, AI, agriculture

Supervisors

Federation University: Dr Birgita Hansen, Dr Nathan Robinson

IBM: Dr Arun Vishwanath, Dr Ram Kolluri

Affiliated research centre

Centre for eResearch and Digital Innovation

5. Edge-computing for smart city applications

Description

Green infrastructure with artificial intelligence-infused decisions in real-time are the requirements for smart cities. This project will leverage edge computing resources, machine learning, and software-defined networking to advance Green communication for collaborative computational intelligence. The outcome of the project will be beneficial to advance Green infrastructure design for smart cities.

Academic background

IT, data science, AI, edge computing, machine learning

Supervisors

Federation University: Associate Professor Gour Karmakar, Dr Venki Balasubramanian

IBM: Dr Arun Vishwanath, Dr Ram Kolluri

Affiliated Research Centre

Centre for Smart Analytics

6. Scheduling the operation of electric mining trucks for minimising carbon footprint

Description

Mining companies are exploring the potential of electrifying haul trucks to reach zero greenhouse gas emissions. But electrical trucks suffer from smaller payload and need recharging, necessitating innovation in truck operation, charging and scheduling without losing productivity. Collaborating with IBM, this project will investigate these problems for mining truck operation.

Academic background

IT, data science, AI, electrical engineering

Supervisors

Federation University: Professor Joarder Kamruzzaman, Dr Tanveer Chowdhury

IBM: Dr Arun Vishwanath, Dr Ram Kolluri

Affiliated research centre

Centre for Smart Analytics

7. Using data analytics to accurately predict different types of risk within supply chains

Description

A supply chain can be represented as a graph of material, process, and information flows, interacting with the environment. In this PhD project we seek to model such representations from available data with an aim to characterise objectively the associated risks.

Academic background

IT, data science, AI, business, supply chains

Supervisors

Federation University: Professor Andrew O’Loughlin, Dr Taiwo Oseni

IBM: Professor Iven Mareels, Dr Chris Butler

Affiliated research centre

Centre for Smart Analytics

8. Combining people and operational practice within an IoT framework to manage supply chain performance

Description

In early 2020 the literature declared "just-in-time" as the main organising principle in supply chains. One pandemic later, it is declared "dead". Neither statement is correct. In this project we explore how "just-in-time" with "resilience" through IoT, and digital twin technology an objective more holistic risk analysis is feasible.

Academic background

IT, data science, IoT, business, supply chains

Supervisors

Federation University: Professor Andrew O’Loughlin, Dr Ben Wills

IBM: Professor Iven Mareels, Dr Chris Butler

Affiliated research centre

Centre for Smart Analytics

9. Optimising Carbon Footprint in Smart Buildings

Description

Reducing energy consumption in the buildings sector will be a key step towards carbon footprint reduction. In collaboration with IBM, this project aims to develop optimised energy management techniques to minimise the energy consumption of buildings through data acquisition, data analytics, optimisation, and control in electrical and computing engineering.

Academic background

IT, data science, AI, electrical engineering background with preferred skills in optimisation, control and simulation

Supervisors

Federation University: Associate Professor Jiefeng (Jerry) Hu, Professor Adil Baghirov

IBM: Dr Arun Vishwanath, Dr Ram Kolluri

Affiliated research centre

Centre for New Energy Transitions