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Research Fellow in Geodynamic Modeling & Surface Processes Job No.: 693562 Location: Clayton campus Employment Type: Full-time Duration: 2-year fixed-term appointment Remuneration: $86,195
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
from Project 1, we will conduct focus groups and interviews with communities in high- and low-risk zones to capture local knowledge, behaviours, and risk perceptions related to AMR. In parallel, key
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PhD Scholarship Opportunity - Processing intelligence for green metals using in situ X-ray characterisation and machine learning Job No.: 693787 Location: Clayton campus Employment Type: Full-time
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This research focuses on developing and evaluating methodologies for the optimal design of control charts within the framework of Statistical Process Control (SPC). The study aims to determine the
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also explore the role of AI to guide or suggest analytics. Patient Data Analysis, Procedure Planning & Organ Segmentation: we will explore immersive XR interaction techniques for patient data
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is carried out within the LHCb collaboration that runs one of the four large experiments at the Large Hadron Collider at CERN as well as towards future collider developments. I supervise a number of
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. We also collaborate closely with researchers at CERN and at Fermilab, and with our LHCb colleagues through the Monash-Warwick Alliance in Particle Physics. Joint projects with an LHCb focus
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Collider at CERN in Geneva. During the past few years vast datasets have been collected, allowing us to probe the Standard Model with world-leading sensitives. Research projects typically involve analysing
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them The Opportunity Join the Finance portfolio as a Business Analysis and Improvement Officer and lead complex analytics, process improvement and change initiatives that support operational excellence
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processing and phase transformation. The candidate will work with experimental datasets from techniques such as electron microscopy, EBSD, X-ray diffraction, X-ray imaging and in situ characterisation