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demonstrated proficiency in qualitative and/or multivariate quantitative research methods and analysis, have experience in supporting the management of a large research program, and have demonstrated an ability
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increasingly one of large-scale systems of people, whose actions and interactions are influenced by our digital, physical and biological environment. The ANU College of Systems & Society (CSS) hosts three
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will work closely with the project’s Chief Investigator, Dr Joanna Melonek, and will be actively involved in experimental design, data analysis, and dissemination of findings through publications and
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charge of the experimental part. The two teams will collaborate closely, and the candidate is expected to integrate their modelling work within the experimental tasks. For more information on the research
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to the principles of equity, diversity and inclusion. Desirable characteristics: Experience of handling large sets of samples from multiple collaborators. Experience with HPAEC-PAD, mass spectrometry and HPLC systems
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning and deep learning algorithms for dynamic systems (sequential or time-series data). Experience
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temperature, flux rate, surface coverage, plasma composition and excitations. The seeding process of a new layer in heteroepitaxy requires large-scale surface modelling with accurate force-field parameters
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metapopulation and/or individual based models Knowledge of Bayesian methods, including Approximate Bayesian Computation Experience with big data analysis and HPC environments Knowledge of additional programming
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experience using Python machine learning and large language models. Experience in machine learning and NLP for automated misinformation detection, social media data scraping and analysis, and human annotation
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using statistical analysis. Experience in project management and working on field projects in the parenting field, and/or experience in working on large scale projects using administrative data. Relevant