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, computational modelling, and data-driven alloy design to: Understand the mechanisms of local austenite-to-ferrite transformation in low-alloy steels; Develop frameworks to predict and control
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the optimisation strategies to enhance the performance of complex machine learning models such as deep learning model and large language model. Applicants need to have strong background and track records of research
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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. While applicants are not expected to meet all criteria, those who demonstrate more of the following attributes will be highly regarded: Strong foundation in AI models: A deep understanding of contemporary
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environmental health economics ; economic behaviour, incentives and preferences in health ; and, economic modelling of health policies and technologies . Employment prospects PhD graduates from the Centre
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cutting-edge research in biochemistry, molecular biology and plant science. The student will be supervised by Dr Tatiana Soares da Costa based at the University of Adelaide. Dr Tatiana Soares da Costa is
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Martin Australia invite applications for a project under this program, advancing robotic perception systems through monitoring of their machine learning models. Run-Time Monitoring of Machine Learning
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for the 2020 paper, Nobiletin exerts anti-diabetic and anti-inflammatory effects in an in vitro human model and in vivo murine model of gestational diabetes, Caitlyn Nguyen-Ngo, Carlos Salomon, Stephanie Quak
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Metallurgy and Corrosion cluster, working within a multidisciplinary team spanning theory, advanced characterisation, and computational modelling. This environment provides an excellent platform for developing
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practice. The expected outcomes of this research program are proof-of-concept for a new model for insomnia precision diagnostics and the targeted therapeutic approaches to better treat this disorder; all