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equivalent qualifications) • Basic knowledge of analytical techniques of rock characterisation • Prior experience in experimental geochemistry (water-rock reactions) is preferred • Some knowledge of
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. Applicants are required to submit a brief research proposal outlining their area of interest and how they intend to advance knowledge in human-animal supports. Selection criteria: Prospective applicants must
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Traditional Owners of the unceded lands on which we work, learn and live. We pay respect to Elders past, present and future, and acknowledge the importance of Indigenous knowledge in the Academy. Read about our
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. Desirable Demonstrated knowledge/experience in: multiphase flow porous media flow microfluidic fabrication and experiments 3D printing surface chemistry Demonstrated programming skills in: Matlab C++ Python
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of escalating terrorist actions in the name of faith, the existing lack of public knowledge about the diversity of views in the Islamic world contributes further to misconceptions and thereby to ethnocultural
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increasing in frequency and severity, there are calls for land management practices to better incorporate Traditional Ecological Knowledge (e.g. cultural burning). This project will apply acoustic metrics
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knowledge on an area of your interest which broadly aligns with the goals of the project. It would suit a candidate with an interest in black media and journalism or issues of justice affecting Indigenous
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of system and data confidentiality and complete any other requirements. Desirable skills: Experience in programming (C, python, or similar). Knowledge in machine learning or distributed systems. How to apply
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experience. Knowledge of quantitative and qualitative research methods highly desirable. Proficiency in a language other than English is highly desirable. Evidence of journal or conference publications
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applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method