Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
statement of interest Academic transcript Applications Close: Sunday 1 March 2026, 11:55pm AEDT Minimum entry requirements: https://www.monash.edu/admissions/entry-requirements/minimum Research webpages
-
networks that can be trained to do machine learning and AI tasks in a similar way to artificial neural networks. In this project you will develop machine learning theory that is consistent with the learning
-
"A picture is worth a thousands words"... or so the saying goes. How much information can we extract from an image of an insect on a flower? What species is the insect? What species is the flower? Where was the photograph taken? And at what time of the year? What time of the day? What was the...
-
. For more information on the work we do, please visit our website: http://www.monash.edu/vpfinance The Opportunity The Strategic Sourcing Manager position is an important role that drives the development and
-
Information Technology Industry-Based Learning Placement Grant This grant is awarded to high achieving students in the Faculty of Information Technology who undertake a placement with an industry
-
We are seeking a motivated PhD candidate to work on unsupervised music emotion tagging within the broader field of affective computing. The project aims to develop reproducible machine learning
-
encoded in computer software and can be used as decision support systems (DSS). These may be used by decision-makers with different domains of expertise than the analysts who built the DSS system. Therefore
-
are: Rapidly identify AMR and predict treatment responses through use of genomics and machine learning in a clinical context Detect healthcare-associated transmission of AMR in real-time and transform outbreak
-
Advisory System, or data from other implantable or wearable devices. This involves consideration of both feature-based machine learning or data science approaches and neural mass parameter estimation
-
species' distributions. This project harnesses research in ecological and agent-based modelling, machine learning, and AI to increase the predictive power of models of species’ distribution shifts via “data