13 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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the molecular mechanisms that enable male germline recovery following damage by genotoxic drugs, and the regenerative responses of SSCs. The research will use in vivo models, organoids, SSC culture, and genomic
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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