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Levin Kuhlmann Research area Machine Learning We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and
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initiatives Key Selection Criteria: PhD in a relevant field with a strong research track record National profile with potential for international leadership Demonstrated success in publishing and attracting
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is highly complex. For the proposed PhD project, experimental data are already available that bring together maps of orientations of such crystals together with the deformation pattern generated during
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through supervision of PhD students, and contribute to the educational and administrative duties of the Department and Faculty such as by taking on course direction roles. As the successful candidate, you
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formula is true or false (EXPTIME vs NP). Can we develop and implement efficient algorithms for this problem? This problem has been attacked using multiple different methods for the past 40 years, without
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, conferences, or equivalent textbooks/teaching resources Ability to lead outstanding research and manage a research team and projects Demonstration of effective PhD or Honours student supervision Proven success
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in diverse, real-world environments. Both classical machine learning methods and deep learning techniques can be employed to tackle this task. This project aims to achieve several objectives: 1
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proven record of relevant teaching experience in a tertiary environment, including experience in developing teaching materials. A deep understanding of contemporary pedagogies and methods is essential
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of the following would be useful: stakeholder engagement, (IT) system design, basic web programming, and technology evaluation. Experience in Indigenous and qualitative research methods is desirable, and Indigenous
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This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain