39 proof-checking-postdoc-computer-science-logic Fellowship research jobs at Monash University in Australia
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industry- and government-friendly materials, public scholarship, and peer-reviewed academic publications The successful candidate will have a doctoral qualification in sociology, science and technology
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computer skills with experience using Microsoft Word, Excel and PowerPoint; and specific experience in working with a range of data management/analytical software. Prior experience, knowledge and expertise
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of Civil & Environmental Engineering is seeking an exceptional early-career researcher to join a nationally significant ARC Discovery Project exploring the sustainable size of cities and the transformative
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delivery of clinical research that directly impacts real-world care. This role is ideally suited to an early career researcher or health sciences graduate with experience in clinical data collection and
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contingent upon the satisfactory completion of all pre-employment and/or background checks required for the role, as determined by the University. Enquiries: Professor Allen Cheng, Allen.Cheng@monash.edu and
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University's Department of Economics is seeking a Research Fellow to join the project titled “Women in STEM: The Longer-Term Effects of Teachers.” This research initiative aims to understand how teacher
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and oral communication skills, including drafting of academic papers and grants High level computer skills with software skills such as Microsoft Office, SAS, SPSS If this sounds like you, we highly
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team as an A Level A Research-Only Academic and contribute to cutting-edge studies in the field of epigenetics, multi-OMIC science, ageing, and exercise. In this role, you will support the university's
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multidisciplinary in nature, cutting across statistics, econometrics, operations research and data science. Specific project outcomes relate to multiobjective optimisation, expected shortfall and conformal inference
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implementation science. We are now seeking an exceptional Research Fellow (Level B or C) to lead innovative methodological research in statistical theory for adaptive variants of longitudinal cluster randomised