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PhD Scholarship Opportunities in Optoelectronic Semiconductors at the Faculty of Engineering and Faculty of Science (Multiple Positions) Job No.: 682543 Location: Clayton campus Employment Type
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The Adelaide Graduate Research School (AGRS) and Australian Energy Market Operator are partnering to create 3 remote internship opportunities for University of Adelaide PhD students, to contribute to various projects. Australian Energy Market Operator (AEMO) manages the electricity and gas...
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Acknowledgement of Country CSIRO acknowledges the Traditional Owners of the land, sea and waters, of the area that we live and work on across Australia. We acknowledge their continuing connection to their culture and pay our respects to their Elders past and present. View our vision towards...
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The Adelaide Graduate Research School (AGRS) and the Adelaide Park Lands Association are partnering to create an internship opportunity for a University of Adelaide PhD students, to contribute to a project that will raise public awareness of this essential green space. The Adelaide Park Lands...
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., finite-element analysis) of scorpion species. Exploring evolution and biodiversity of Australian scorpions through methods such as phylogenetic analysis, geometric morphometrics, distribution modelling
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methods. 4. Explanatory qualitative strand to understand barriers and reasons for non-participation in vulnerable groups. 5. Synthesis of mixed methods findings to generate recommendations for promoting
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or other drug use is involved, is responded to by multiple systems, including child protection, health, family services, juvenile justice and police. The public health research project will use individual
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publication Strong programming skills and familiarity with machine learning or finite element modelling Not currently receiving another scholarship of equal or higher value Application process Future student
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-team and contribute to a clinical trial research project of national significance for youth mental health. The project applies modern data science methods to build explainable and integrated machine
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data science methods to build explainable and integrated machine learning models that can be utilised by health services to make real-time, data-informed clinical decisions in youth mental health care