256 developer-"https:"-"https:"-"https:"-"https:"-"https:" positions at Monash University
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Work Disability Benefit Systems for Contemporary Australian Society. The program comprises three interconnected streams: Program 1 identifies how work disability develops through a national cohort study
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. The Opportunity This interdisciplinary PhD project between IT and the social sciences will explore the development of computational approaches for detecting and analysing misogynistic backlash ecosystems across
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to instantiate it. This project will develop a verifiable, causal, and uncertainty-aware world model designed specifically to function as a safety substrate for downstream AI agents. The student will: Develop
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confidence and clarity. Responsibilities span end‑to‑end event and project management, including concept development, planning, delivery, evaluation and reporting. You’ll provide authoritative advice to senior
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I supervise projects in particle physics. My main emphasis is on phenomenology, comparison of predictions with experimental measurements. I follow developments in flavour physics: weak decays
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developing policies, procedures and case management practices while providing expert advice to stakeholders. Strong analytical, investigative and problem-solving skills within a security or law enforcement
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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
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My research focusses on understanding stars: their evolution and chemical composition, and how they move throughout our galaxy. Most of what we know about the universe comes from starlight, but
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., hyperspectral) and climate reanalysis data, calibrated with in situ field data, laboratory analysis, and numerical models of landscape evolution. Apply today to spearhead groundbreaking landscape evolution
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Minimum Message Length (MML) is an elegant information-theoretic framework for statistical inference and model selection developed by Chris Wallace and colleagues. The fundamental insight of MML is