Sort by
Refine Your Search
-
Listed
-
Employer
- Monash University
- Charles Sturt University
- The University of Queensland
- RMIT University
- University of Sydney
- CSIRO
- James Cook University
- Macquarie University
- University of New South Wales
- CHARLES STURT UNIVERSITY
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Federation University Australia
- Curtin University
- Flinders University
- University of Tasmania
- ADELAIDE UNIVERSITY
- MONASH UNIVERSITY
- RMIT UNIVERSITY
- The University of Western Australia
- UNIVERSITY OF SYDNEY
- Victoria University
- WESTERN SYDNEY UNIVERSITY
- 12 more »
- « less
-
Field
-
paradigms rely on a fragile "closed-world" assumption: that the unlabeled pool perfectly reflects the distribution of the labelled seed set. In real-world deployments, this is rarely true. Data streams
-
Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage
-
hospital or population often fail when applied elsewhere due to distributional shifts. Since acquiring new labeled data is often costly or infeasible due to rare diseases, limited expert availability, and
-
management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research. Key Components and Example Scenarios Predictive Resource Allocation and Load
-
-functionally within the Marketing and Communications team, you will support the delivery of a diverse program of talks, outreach and events aligned to the university’s strategic goals . You will work
-
Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
-
, from swarm robotics to mesh networks. The prototypical model system for the investigation of self-organised task allocation are social insect colonies, such as bees and ants. They are able to distribute
-
skills in Git, CI/CD pipelines, and infrastructure automation tools such as ArgoCD, Ansible, Azure DevOps, and Github. Solid foundation in Linux systems, distributed systems and high-performance computing
-
-on experience with large-scale data processing using distributed computing frameworks. Strong understanding of data performance optimisation techniques. Proficiency in Python and SQL, with experience using Git
-
material, such as event proposals, run sheets, briefing notes and guest lists and ensure these are distributed in a timely manner. Collaborate with Advancement colleagues on pre‑ and post‑event