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motivated and talented computational scientist to join the ice sheet modelling team at the Institute for Marine and Antarctic Studies (IMAS) , University of Tasmania, working closely with Australia’s Climate
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diabetes management system using a mobile app to rate foods based on the glycaemic response of an individual. AI models will be trained on both the food intake and blood glucose data, and learn from
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. Collaborate with the lead modeller to translate requirements into production-ready outputs. Partner with stakeholders to understand and support their data infrastructure needs, troubleshooting and resolving
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research program for understanding the formation of reaction intermediates during the pyrolysis of biomass and model components, focusing on characteristics and reaction mechanisms and end effects. Student
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research and cybersecurity research, notably one current direction is on advancing the latest generative AI models for cybersecurity, or vice versa: using cybersecurity to attack AI. The student is free
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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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networks and/or probabilistic graphical models; computer vision such as re-identification and/or tracking. Good publication record in Q1 machine learning and/or computer vision conferences and/or Q1 medical
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, infer arbitrarily closely to any model underlying data. We endeavour to do the following: Apply MML to test datasets (degenerate motifs of 6-12 base pairs and with 1 to 2 variable nucleotides) Apply MML
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Undertake testing on new AI models and systems to ensure reliability and alignment with experimental goals. Liaise with other engineers and stakeholders to finalise AI models and systems for use in
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the Learning Enhancement Team. This includes data extraction, cleaning and analysis; student load modelling; plan, program and curriculum learning visualisation and analysis; thematic analysis of student