The Academy joint PhD research projects are well defined and developed by a collaborative team of researchers at UQ and IITD. Selected PhD students working on a project will be supervised by a joint supervision team of UQ and IITD academics.
To apply, select a ’Position Open’ project. You can nominate up to two projects in your EOI form.
The main aim of the proposed work is to develop a deep fundamental framework to engineer and predict properties of 3D nonwoven materials for next-generation lightweight composites.
In this project we will explore adversarial attacks on deep neural networks for the visual recognition tasks. We will be focusing on the practical attacks in which the adversary causes large unrestricted perturbation in an image, thus managing to fool both the model as well as the human observer.
This project aims to advance theory and practice on the uptake of sustainable construction materials through a case study of the potential introduction of low-carbon Limestone Calcined Clay Cement (LC3) to Pacific Small Island Developing States (PSIDS).
This project uses Nanopore sequencing to map micro-exon splicing events at a single cell level in human induced pluripotent stem cell derived brain organoids to gain insight into how abnormal micro-exon splicing leads to neurodevelopmental defects that cause epilepsy.
The production of chemicals and energy from plants is a key alternative to fossil carbon. This project will use technoeconomic analysis to evaluate the potential use of sugarcane, an abundant source of biomass in both India and Australia, to produce a range of high value end products.
The project aims to develop new class of light-emitting devices operating at very high current densities.
Novel materials and systems are being introduced into the built environment at an increasing rate. These often result in unforeseen consequences in the event of accidents, impacting on resilience of buildings. This project identifies the contribution of novel materials to building resilience.
The resource-constrained nature of internet-of-things (IoT) make them vulnerable against artificial intelligence (AI)-assisted cyber-attacks. In this project, we will develop an adversarial AI-driven framework for designing and evaluating an intrusion detection system that can detect such attacks.
This project addresses the cybersecurity of critical infrastructure by designing automated vulnerability detection test kits for heterogenous industrial control systems (ICS) and industrial Internet of Things (IIoT) equipment. The project will utilise the facilities of the UQ Industry 4.0 TestLab.
This project will analyse the resilience of communities vulnerable to fire hazards. With a specific focus on studying the resilience of communities with different vulnerability profiles, the aim of the project will help to inform policy and resource allocation to improve fire safety outcomes.