523 quantum-physics-"https:" "https:" "https:" positions at Monash University in Australia
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Multi-View Learning in CV and NLP Robust Active Learning Under Distribution Drift Data-Efficient Deep Learning for De Novo Molecular Design from Analytical Spectra Hybrid Quantum–Classical Algorithms
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Engineering, Solid-State Physics, Chemical Engineering, or a closely related discipline. Applicants must demonstrate: A strong interest in thermodynamics, kinetics, and microstructure evolution in materials
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I supervise a wide range of PhD projects on experimental research into the electronic properties of novel quantum materials including topological insulators, graphene, and other atomically thin two
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will receive a Research Living Allowance, at current value of $37,145AUD per annum 2026 full-time rate (tax-free stipend), indexed plus allowances as per RTP stipend scholarship conditions at: https
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Quantum–Classical Algorithms for Scalable Data Systems and Intelligent Analytics Unsupervised Music Emotion Tagging (Affective Computing) Authorised by: Marketing, Faculty of IT , Monash University
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Learning for De Novo Molecular Design from Analytical Spectra Hybrid Quantum–Classical Algorithms for Scalable Data Systems and Intelligent Analytics Unsupervised Music Emotion Tagging (Affective Computing
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inspection of and interaction with mechanical devices. Examples might include aircraft maintenance or other complex workshop environments. We term work in such situations as "physically embedded" in
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Engineering, Solid-State Physics, Chemical Engineering, or a closely related discipline. Applicants must demonstrate: A strong interest in thermodynamics, kinetics, and microstructure evolution in materials
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, Metallurgical Engineering, Mechanical Engineering, Physics, or related disciplines. Candidates should demonstrate a strong interest in thermodynamics and kinetics of solid-state phase transformations, as
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Understanding factors related to student retention and experience in physics and astrophysics major units. Using quantitative (surveys) and qualitative data (interviews with students) this project