Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
- Computer Science
- Medical Sciences
- Economics
- Engineering
- Science
- Business
- Humanities
- Law
- Biology
- Materials Science
- Mathematics
- Philosophy
- Education
- Linguistics
- Arts and Literature
- Environment
- Psychology
- Chemistry
- Sports and Recreation
- Design
- Earth Sciences
- Electrical Engineering
- Social Sciences
- 13 more »
- « less
-
Centre for Health Economics, Monash Business School, Integrated PhD Program 2026 Fully Funded 4.5-Year PhD in Health Economics - Monash University (Melbourne, Australia) Job no.: 625101 Location
-
Anomaly detection methods address the need for automatic detection of unusual events with applications in cybersecurity. This project aims to address the efficacy of existing models when applied
-
provide multiple competitive scholarships funded by a national elite HDR training program: Data61 Next Generation Graduate (https://www.csiro.au/en/work-with-us/funding-programs/programs/next-generation
-
time. In this project, we propose a method for identifying and classifying such emerging asynchronous trends. The goal is to be able to predict how a new emerging trend will develop using similar
-
analytical imaging methods, then working with collaborators to apply these methods to biomedical research, diagnostic imaging and beyond. Research projects vary from purely theoretical, to computational
-
operators for these notions. Over the past fifty years, such non-classical logics have proved vital in computer science and logic-based artificial intelligence: after all, any intelligent agent must be able
-
Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
-
cosmolgy, galaxy evoltion and stellar astrophysics. Students in my group primarily perform numerical simulations of stars, in order to study broad questions related to the origin of the elements in
-
Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
-
field” imaging techniques to solve many important problems in biology and change clinical practice in respiratory medicine. Our ongoing research program involves developing new imaging technologies