12 molecular-modeling-or-molecular-dynamic-simulation Fellowship positions at University of Adelaide
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plant molecular physiology for major crops in South Australia. Outputs from this investment will lead to new research discoveries related to fundamental understanding of molecular biology, physiology and
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Technology (SET) This position plays a vital role in developing advanced theoretical tools and models for superconducting quantum materials research, contributing to leading-edge defence-related technologies
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contribution of 17% superannuation applies. Fixed term position for 24 Months. Postdoc opportunity - Open for Applications Until Filled. We are seeking a dynamic and motivated Postdoctoral Fellow in Ecological
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or knowledge of invertebrates within southern Australian agricultural systems Experience with molecular methods for species identification and/or population genetics. Knowledge of statistical analysis, data
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decision making in contested and dynamic environments, considering a broad range of modelling and simulation and software engineering research approaches. The ideal candidate will enjoy working in a team
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government and industries in South Australia, and overseeing Centre performance. The appointee will also be supported to establish and build a dynamic research program in machine learning or artificial
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quantitative methodologies, especially for instance in the areas of econometrics (microeconometrics), statistics, and economic modelling. Demonstrated knowledge of statistical and econometrics software packages
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support in different initiatives related to the prevention, diagnosis and management of different conditions in primary care. Working with a dynamic team of primary care researchers, you will play
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closely with an industry partner, you will generate cutting-edge insights into protein structures and protein-ligand interactions using in silico approaches such as bioinformatics, modelling and docking
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learning and one or more of the following: transformer networks, implicit neural functions, graph neural networks and/or probabilistic graphical models; and causal inference. • An outstanding publication