262 developer-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at Monash University
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
resources to avoid downtime, adjusting dynamically as traffic fluctuates. For researchers and students, this component focuses on developing ML models to predict resource needs, improving load distribution
-
, yielding negligible performance gains or even inducing catastrophic forgetting. To bridge the gap between theoretical AL and real-world deployment, this PhD project will develop resilient active learning
-
for intelligent systems to guard their execution and evolution. Required knowledge - deep learning - software development lifecycle
-
aims to improve efficiency and privacy of federated learning for mobile health sensing data by proposing a multi-level (mobile-edge-cloud continuum) federated learning architecture and develop context
-
these challenges, calling for the development of responsible AI systems that are transparent, trustworthy, and aligned with human values in educational contexts. This PhD project aims to design, develop, and
-
be considered for the Monash International Leadership Scholarship. As a scholarship recipient you will receive a 100% tuition sponsorship for the duration of your degree, and the opportunity to develop
-
-disciplinary team of clinician scientists and computer scientists to develop diagnosis/predictive/treatment/robotics surgery models of diseases of interest using multimodal medical data, consisting of images
-
these issues is critical for building trustworthy multimodal AI systems. Research Objectives The goal of this PhD project is to develop scalable Bayesian uncertainty estimation frameworks for single- and multi
-
can occur that are very different to the macroscopic world. Our group develops methods to measure and ‘see’ this atomic detail using some of the world’s most powerful electron microscopes. We apply
-
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