23 finite-element-analysis Fellowship positions at The University of Queensland in Australia
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Completion or near completion of a PhD in Engineering (mechanical, aerospace, civil, petroleum) or Science (applied physics, applied mathematics) with a strong background in CFD and the finite element method
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translation. University of Queensland clinical Trials capability (ULTRA) Program Randomised controlled trials (RCTs) are an important component of the healthcare system as they provide gold standard, most
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in human-research related fields, specifically using some of the sensors involved in the project (ultrasound, EMG, motion capture, force), and the ability to perform basic data processing and analysis
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collection, analysis, and the preparation of publications in leading academic journals. Analyse longitudinal survey data, using a variety of advanced statistical analysis techniques Collaborate on grant
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technologies that support decarbonisation and clean energy goals. The successful applicant will contribute to the computational modelling and design of Prussian blue analogues, spin-crossover metal-organic
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development, validation and sample analysis studies including modifying existing research techniques to optimise the outcomes of the research endeavour. Contribute to and/or draft elements of research proposals
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many-body methods for multi-valence-electron atoms, with a focus on transition metals of interest for spin-crossover metal-organic frameworks (MOFs). The applicant will be involved in the development
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Lucia, with occasional work at Gatton and possible travel to other QLD sites required. Key responsibilities will include: Research: Carry out trait mapping using GWAS analysis and functional genomics
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. Demonstrated experience with UHV instrumentation, particularly low-temperature STM/AFM. Expertise in STS and analysis of local electronic structure. Experience with STM-based measurements of superconducting
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Quantitative Genetics, Statistical Genomics, Computational Biology, Plant Breeding, or a related field. Strong expertise in GWAS and post-GWAS analysis (e.g., fine mapping, gene network modelling, GWAS boosting