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Postdoctoral Research Fellow: Computational Methods for Fluid-Granular Flows Job No.: 690226 Location: Clayton campus Employment Type: Full-time Duration: 3-year fixed-term appointment Remuneration
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, and collaborative development experience with artificial intelligence or machine-learning tools and methods, particularly their application to mathematical research or software development. This may
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reactions, catalyst surfaces and interfaces, reaction mechanisms, activity and selectivity develop reproducible atomistic simulation and high-throughput workflows using Python, ASE and relevant DFT software
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methods to large datasets using advanced statistical methods and software such as R, Stata, or SAS. Evidence of successfully seeking, obtaining, or managing external research funding. Experience engaging
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Ling Yap Dr Md Mijanur Rahman Key skills and experience required: A PhD in Biostatistics, Epidemiology, Data Science, or related discipline and/or relevant work experience Have contributed to competitive
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delivery, public health principles and chronic disease prevention and health promotion methods, but also a passion for research, community engagement, and collaborative problem-solving. Experience in, and a
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health service delivery, public health principles and chronic disease prevention and health promotion methods, but also a passion for research, community engagement, and collaborative problem-solving
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record and demonstrated research leadership. We welcome applications from outstanding recent PhD graduates as well as candidates with postdoctoral experience who are seeking to establish or further develop
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computational methods for analysing spatial omics data work with interdisciplinary collaborators to apply methods to real biological data and interpret results contribute to the supervision and mentoring of PhD
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PhD Opportunity PhD research on “Progressive Voices/Movements in Islamic Theology in the (Post-)Secular Society of 21st Century” The main purpose of this recommended research project is to promote