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reinforcement machine learning models to enable efficient transpilation of quantum circuits on a wide range of quantum processor modalities and topologies. The key aim of the project is to minimise resource
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(density functional) theory, tight-binding and model Hamiltonians, quantum transport and machine learning, run on national supercomputers (NCI Gadi, Pawsey Setonix) and Monash's NVIDIA Blackwell AI system
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in the translation of research into real-life products A desire to learn new skills in complementary disciplines. Candidates should have recently successfully passed an English language test Selection
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English language requirements . Not have previously completed a PhD. Be able to commence the Program in the year of the offer. The ideal PhD candidate will have: A strong background in machine learning, deep
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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://www.monash.edu/graduate-research/future-students
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semiconductors” (https://irtg-optexc.de/ ). As part of this program there is the opportunity for students to undertake fully funded joint degrees with the University of Bayreuth in Germany in the areas
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of the offer. The ideal PhD candidate will have: A strong background in machine learning, deep learning, and software programming Proficiency in Python and machine learning frameworks such as PyTorch
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View our FAQ section View Hints & Tips to help with your application Preview Application Form By choosing to continue, you agree to the privacy policy. Show Privacy Policy Where to apply Website https
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scholarship conditions at: https://www.monash.edu/graduate-research/future-students/scholarships/scholarship-policy-and-procedures . A tuition fee scholarship and Single Overseas Health Cover (OSHC) will be
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motivation Testing whether the system performs equitably across cultural and language groups Training spans intervention design, trial methodology, human-computer interaction, and applied machine learning