376 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "University of Waterloo" positions at Monash University in Australia
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
Many machine learning (ML) approaches have been applied to biomedical data but without substantial applications due to the poor interpretability of models. Although ML approaches have shown
-
the brain. This wouldn't be a typical machine learning PhD, as many aspects can only be examined on a philosophical and theoretical level. There may be scope to implement aspects in the ideas you develop
-
Machine learning, dynamical systems theory, control theory, signal processing, network theory, neuroscience are all relevant and a student should have strong knowledge in at least one of these and a
-
reinforcement learning. In International conference on machine learning (pp. 2107-2128). PMLR. - Péron, M., Becker, K., Bartlett, P., & Chades, I. (2017, February). Fast-tracking stationary MOMDPs for adaptive
-
" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: haoran.ren@monash.edu.
-
and multimodal applications. Required knowledge Candidates are expected to have a solid background in machine learning and Natural Language Processing. Research experience in multimodal research is
-
performance, is normally attributed to their learning capabilities. A learning solver gradually deduces and remembers new information about the decisions previously made, which can be reused in the future
-
while inferring underlying physiological changes. Required knowledge Machine learning, dynamical systems theory, control theory, signal processing, time series analysis, neuroscience are all relevant and
-
interested in, please apply via our website and complete the online expression of interest form https://www.monash.edu/ research-ethics-and-integrity/ animal-ethics/accordion- content/accordion_1 For further
-
Over the past decades, we have witnessed the emergence and rapid development of deep learning. DL has been successfully deployed in many real-life applications, including face recognition, automatic