Sort by
Refine Your Search
-
-field imaging of dynamic processes" "Multi-scale X-ray speckle-based imaging" "Spectral X-ray speckle-based imaging" "Single-shot multi-projection X-ray phase-contrast imaging" "X-ray virtual histology
-
" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: haoran.ren@monash.edu.
-
Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
-
materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
-
processes that arise in light–matter interactions, and to use these frameworks to extract fine spectral or temporal information from weak or structured optical fields. The successful candidate will work
-
focus on quantum channel discrimination for high-resolution spectroscopy and AC field sensing. The project aims to develop theoretical frameworks for distinguishing closely related quantum processes
-
frameworks for distinguishing closely related quantum processes that arise in light-matter interactions, and to use these frameworks to extract fine spectral or temporal information from weak or structured
-
frameworks for distinguishing closely related quantum processes that arise in light-matter interactions, and to use these frameworks to extract fine spectral or temporal information from weak or structured
-
focus on quantum channel discrimination for high-resolution spectroscopy and AC field sensing. The project aims to develop theoretical frameworks for distinguishing closely related quantum processes
-
with a new cutting-edge quantitative-trading company to push the frontiers of AI-aided decision-making in quantitative trading processes. As a PhD candidate you will: Design next-generation trading