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to pushing the boundaries of eDNA science through exploring eDNA enrichment, quantification and modelling/software methods. We are seeking a creative and motivated Early Career Postdoctoral Research Fellow to
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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on specifications and contribute to the evolution of the existing code-base in consultation with the other members of the systems modelling team. Perform duties at a skill level that requires a degree, normally with
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: Bayesian Machine Learning – Led by Dr Thang Bui, this project focuses on sequential decision-making and bridging deep learning theory and practice. Applicants with expertise in probabilistic modelling
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on the development of a national-scale land use–transport interaction (LUTI) model. This model will be central to the project, generating critical indicators such as job accessibility and population
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familiarity with formal software modelling and design techniques, cloud and containerisation technologies, agile software development methodologies and practices, high performance computing (HPC) systems
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the diagnosis and management of people with skin cancer in the Loddon Mallee Region (LMR) by developing and testing a model that builds primary care capacity and improves access to care in a financially viable
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research aimed at quantifying or modelling the state and function of soils across spatially variable landscapes. Applied experience in using statistical and mathematical tools to analyse and interpret soil
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facilities, and sophisticated modelling to support researchers, innovators, and policymakers in shaping the energy landscape of the future. NEAC includes: The Living Lab – a real-world data network of homes
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heritage Camperdown campus, and welcomes a hybrid working model (minimum 3 days per week in the office). We are a trusted and strategic business unit that embraces innovation and continuous learning