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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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large-scale whole genome sequencing datasets, applying cutting-edge bioinformatics approaches to study structural variants. The fellow will play a key role in advancing our understanding of evolutionary
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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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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
to work with a world class University that undertakes cutting edge research and has a strong tradition in research-led teaching excellence. We offer: For more information on a range of lifestyle, financial
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 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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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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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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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 months ago
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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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