Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
This project focuses on brain network mechanisms underlying anaesthetic-induced loss of consciousness through the application of simultaneous EEG/MEG and neural inference and network analysis
-
Research Program Officer – Blood Synergy Program Job No.: 684863 Location: 553 St Kilda Road, Melbourne Employment Type: Full-time Duration: 12 month fixed-term appointment Remuneration: $96,768
-
centre networks. It combines strategic planning with deep technical expertise to ensure our research computing environments are future-ready, reliable, and secure. You will be responsible for ensuring
-
for tomagraphic imaging in tissue Neural network correction of distortions in acoustic transducers web page For further details or alternative project arrangements, please contact: alexis.bishop@monash.edu.
-
trials within the Computational Neuroscience Laboratory and the Addiction and Impulsivity Research group . This position will drive participant engagement across clinical trials of psychedelic-based
-
. National Road Safety Partnership Program (NRSPP) offers a collaborative network to support Australian businesses in developing a positive road safety culture. It’s about saving lives without the red tape
-
Research Officer – Australia Dementia Network Registry Job No.: 683426 Location: 553 St Kilda Road Employment Type: Full-time Duration: 12 month fixed-term appointment (with potential extension
-
of experiments, analysis of neuropsychological and cognitive data and application of computational models. It also contributes to scientific publications and supports collaboration within a network of researchers
-
to lead the design and delivery of high-performance, scalable data systems powering real-time condition monitoring across rail networks in Australia and internationally. This is a professional (non-academic
-
neural networks, we aim to improve the interpretability and robustness of reconstruction techniques. Another exciting direction involves self-supervised learning, which reduces reliance on fully labeled