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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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analyse and harmonise large-scale claims, service and payment data across multiple jurisdictions—delivering insights that can influence national injury prevention and health policy. You’ll be part of
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
, data analysis, and dissemination of findings through publications and presentations. The successful candidate will have the opportunity to contribute to a collaborative and dynamic research environment
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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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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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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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projects including complex data analysis (e.g., telemetry or genetic data) and paper writing The candidate will be working remotely (i.e. off-campus) providing excellent work flexibility Position Overview
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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