Sort by
Refine Your Search
-
results in a broad range of indoor applications including emergency services, public services, in-store advertising, shopping, tracking, guided tours, and much more. In this project, we are interested in
-
data, such as temporal dependencies and lack of rich semantic make it challenging to leverage these models directly for time series tasks. Building upon our exisiting research [1], our objective is to
-
the power of LLMs to develop advanced computational methods for the detection and mitigation of misinformation and disinformation. More specific objectives are: To investigate the effectiveness of large
-
Average Mark (70%) Applicants must have: achieved a track record of academic excellence and a commitment to academic success in their Science degree the ability to succeed in the face of adversity and to
-
, achievement, and contribution to organisations and/or their community(s). Demonstrate a track record and passion for a future career in finance, banking and/or economics. To retain this scholarship: You must
-
The primary objective of this project is to enhance Large Language Models (LLMs) by incorporating software knowledge documentation. Our approach involves utilizing existing LLMs and refining them
-
groups and the longevity and sustainability of recordkeeping practices. This PhD project will contribute to the objectives of the Australian Research Council-funded DECRA project Recordkeeping
-
. This will be achieved through frequency domain and time domain state and parameter estimation techniques to infer model states and parameters in real time to simultaneously track the anaesthetic brain states
-
issues in Mobile Apps) that have the largest impact on end-users and humanity. Finally, this project will leverage a multi-objective optimisation approach to find a set of optimal QA prioritisation
-
the last five (5) years, OR will complete an undergraduate degree in the year of application You have achieved or are on track to achieve an average result of H1 or H2A equivalent (approx. 75%) Intending