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work that underpins the scientific research of the collaboration. Research Title: Process Modeling using Physically Informed Machine Learning The work will entail: § Designing and training physics
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and Host Area The School of Computing has a strong foundation in computing and information sciences at ANU. We are a transformative centre for research in artificial intelligence and machine learning
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changes and established markers for Alzheimer's disease. The project may also include machine learning methods to estimate individuals' biological age. The project is based on existing data from a prominent
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use of molecular assays such as real-time PCR and next-generation sequencing to study the prevalence of infectious diseases in the target study populations. Learning about sample testing under a
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volumes, there is a plan to utilize modern machine learning strategies like "physics-informed neural networks." One of the main advantages of this approach is that measurements and observations made
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Qualifications Experience with machine learning and AI models. Ability to communicate clearly with colleagues, Data presentations Scientific publication experience Special Instructions Priority Application Review
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thrombosis and lung injury in Sickle Cell Disease. The prospective candidate will have the opportunity to learn state-of-the-art techniques such as Multi-Photon-Excitation intravital microscopy of the lung and
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condition leading to medical discharge following combat related trauma in our military. Learning opportunities include, but are not limited to: exposure to various aspects of pre-clinical research by
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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these powerful tools, and support learning in disciplinary or interdisciplinary contexts. In addition, the nature of the interaction between human and machine triggers new questions about the locus of agency and