436 engineering-computation-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" positions at Monash University
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
- Computer Science
- Medical Sciences
- Engineering
- Economics
- Business
- Science
- Law
- Linguistics
- Materials Science
- Arts and Literature
- Biology
- Education
- Humanities
- Mathematics
- Environment
- Psychology
- Chemistry
- Earth Sciences
- Electrical Engineering
- Philosophy
- Sports and Recreation
- Design
- Social Sciences
- 13 more »
- « less
-
This project draws on a recent Dagstuhl Seminar (https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=18322) that brought together leading experts from industry and academia, including those who
-
-centric healthcare innovations—that align with the University’s mission of improving health outcomes through technology. If you are excited about the opportunity to shape the future of data-centric
-
, Faculty of Science or Faculty of Engineering at a Monash campus in Australia. Experiencing financial hardship .and be from a regional or remote area Benefits $10,000 per annum (48 credit points of study
-
Monash Leaders Scholarships Monash Leaders Scholarships are awarded to applicants that demonstrate leadership and commitment to give back to the community through the Access Monash Mentoring program
-
each year (subject to suitable applicants) one in the Humanities and Social Sciences (HASS) disciplines one in the Science, Technology, Engineering and Mathematics (STEM) disciplines Selection criteria
-
classification'', Computer Journal, Vol 11, No 2, August 1968, pp 185-194 Wallace, C.S. and D.L. Dowe (1999a). Minimum Message Length and Kolmogorov Complexity, Computer Journal (special issue on Kolmogorov
-
their potential at Monash University. The scholarship program amplifies diversity in STEM through empowering scholarship recipients to achieve academic success. Total scholarship value $6000 Number offered 10 See
-
the different actors' beliefs and intentions. We will study the properties of such explanations, present algorithms for automatically computing them as well as extensions to existing frameworks and evaluate
-
. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch
-
. This is a very broad topic that allows the student to choose a scope (e.g. the nature of the technology, participant demographics, visualisation) of interest to them.