276 machine-learning-"https:"-"https:"-"https:"-"https:" positions at Monash University
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
, creating realistic audio deepfakes has become easier, raising concerns about misinformation and privacy. To combat this, this project aims to develop machine learning models to analyse audio features such as
-
Learning Support Officer - Indigenous Job No.: 698220 Location: Clayton campus Employment Type: Full-time Duration: Continuing appointment Remuneration: HEW 6 $100,155 - $108,106 (plus 17% employer
-
On-device machine learning (ML) is rapidly gaining popularity on mobile devices. Mobile developers can use on-device ML to enable ML features at users’ mobile devices, such as face recognition
-
. Scientific Contribution Our group has strong publication record of 100+ first or senior author top-tier (ERA ranking A*/A) journals and technical conferences in the machine learning and medical AI field. His
-
Manager, Learning & Capability Job No.: 694882 Location: Mulgrave Employment Type: Full-time Duration: Continuing appointment Remuneration: $145,062 - $153,976 pa HEW Level 09 (plus 17% employer
-
PhD Scholarship Opportunity - Processing intelligence for green metals using in situ X-ray characterisation and machine learning Job No.: 693787 Location: Clayton campus Employment Type: Full-time
-
Large language models increasingly adapt using external feedback to alter inference-time behaviour, persistent state or model parameters. However, the signals that drive these changes may be noisy, biased, incomplete, delayed, correlated with the model’s own errors, or progressively underused...
-
Machine learning has recently made significant progress for medical imaging applications including image segmentation, enhancement, and reconstruction. Funded as an Australian Research Council
-
model with each SNP independently, perhaps adjusting for other covariates such as age and sex. This project will focus on developing and applying novel machine learning and AI methods to improve
-
of foundation models in natural language processing and computer vision, this project seeks to develop general-purpose graph foundation models capable of learning transferable representations from large-scale