Research projects in Information Technology

Project description: 

Project description: On behalf of the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), we will establish the role of artificial intelligence (AI) deep learning to improve the prediction of clinical and longer-term patient-reported outcomes following distal radius (wrist) fractures. The PRAISE study will, for the first time, use a flexible three-stage multimodal deep learning fracture reasoning system to unlock important information from unstructured data sources including X-ray images, surgical and radiology text reports.

Student satisfaction with feedback is consistently low in higher education, and there is a lack of understanding regarding how students interpret and interact with feedback. Learning analytics promises to enhance feedback practice by providing real-time data and insights into learning behaviour and outcomes, so as to inform educational interventions. However, the feedback loop remains open without an understanding of how students make use of the received feedback or the #sustainability of such feedback practice.

This PhD project is funded by a DHCRC project "Actionable data for clinicians and external accreditors in support of quality care provision and continuous accreditation". The successful applicant will work as part of a larger research team on this project which includes multiple industry partners.

The United Nations Development Programme has identified access to information as an essential element to support poverty eradication. People living in poverty are often unable to access information that is vital to their lives, such as information on their entitlements, public services, health, education or work opportunities. Timely access to information is essential to perform many economic, social and leisure activities. In today’s digital age, information is more and more often provided in digital form.

Complex questions are those that involve discrete, aggregate operators that operate on numbers (min, max, arithmetic) and sets (intersection, union, difference). Recent advances in complex question answering take a neural-symbolic approach and combine meta-learning and reinforcement learning techniques [1,2,3]. On the other hand, the generation of complex questions, the dual problem, is less explored. Recent works on knowledge graph question generation [4,5] have mainly focussed on multi-hop questions.

This scholarship will provide a stipend allowance of $29,000 AUD per annum for up to 3.5 years, plus $4,000 travel allowance. If you are currently in Australia you are strongly encouraged to apply. If successful, you will join Dr.

The Australian Research Council (ARC) Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA) is seeking applications for ten ARC fully-funded PhD projects with generous top-up scholarships.

We're looking for talented students with a background in mathematics, computer science, statistics, economics, engineering or other related fields. These positions are offered across OPTIMA's nodes located at Monash University or The University of Melbourne. Projects will be available from June 2021 onwards.

The proposed PhD project aims to build a machine learning/deep learning-based decision support system that provides recommendations on precision medicine for paediatric brain cancer patients based on clinical, genomics and functional dependency data (CRISPR, drug screens).

Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need to devise adaptive models that take into account the dynamically changing characteristics of environments and detect anomalies in ‘evolving’ data.