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learning, climate science, physics, mathematics, statistics or related fields. Evidence of good understanding (or capacity to develop understanding) of statistics and ML. Evidence of a good understanding (or
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
designing and conducting studies, applying advanced statistical methods, publishing in high-impact peer-reviewed journals, and presenting findings at national and international conferences. The role will
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compare these insular giants with their modern mainland relatives using a variety of methods including statistical shape analysis and virtual biomechanical simulation. About the role The main purpose
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, including data processing, harmonization, and statistical analysis workflows. Data translation: Lead technical data activities across laboratories and omics groups; works with investigators, bioinformaticians
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the context of technology sectors. He/she should have good programming/coding skills such as SQL, Python and/or Java and be competent with statistical packages like STATA or R (STATA preferred). Skill and
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The post holder will be responsible for leading on the data analyses in A-STAR as well as data management, supported by a Research Associate in statistics and several data managers. The post holder will be
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use the algorithms in practice, when little to no assumptions can be made on the data. Required Qualifications PhD in computer science, mathematics, statistics, or related fields (by the start date
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to measure nutrient availability, soil microbial activity, and plant responses. Data management and analysis: Collect, manage, and analyze experimental data using statistical and modeling tools. Interpret
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. or M.D./Ph.D. in areas such as Data Science, Statistics, Computer Science, Epidemiology, Environmental Health, or a related field. Demonstrated expertise in large scale analysis and familiarity with health
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of causal inference and machine learning models linking transport, energy, and environmental outcomes • Conduct advanced econometric and statistical analyses of large behavioural and spatial datasets