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magdalena.plebanski@rmit.edu.au . A copy of electronic academic transcripts A CV that includes any publications/awards and the contact details of two referees To apply, please submit the following documents
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: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and biology of infection. For more information, please see https://www.scilifelab.se/data-driven
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Supervisors: Prof. Gabriele Sosso, Dr Lukasz Figiel, Prof. James Kermode Project Partner: AWE-NST This project utilises advancing machine learning techniques for simulating gas transport in
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biological physicists who ask how physical mechanisms shape functional biological patterns. We combine statistical physics, nonlinear dynamics, mathematical modeling and data-driven simulation with physics
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-driven simulation with physics-inspired data and image analysis, often in close collaboration with experimental partners, to identify physical principles behind biological dynamics and self-organization
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Zhongzheng Wang as your proposed principal supervisor, and copy the link to this scholarship web page into question two of the Financial Details section. About the scholarship Background Two PhD positions
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of corrosion at the interface between metals (e.g., Ni) and molten salts (FLiNaK). These systems are gaining interest for their potential in molten salt nuclear reactors. You will combine atomistic simulations
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include novel solar cells, field amplification for highly sensitive spectroscopy, biosensing applications where complex detection processes are made simpler, and even on-site sample examination using
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music events Develop and apply mixed-method approaches, such as spatial mapping, qualitative interviews, and media analysis Collect and analyze data on audience movement and mobile phone use in different
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combinations of approaches including electrophysiology, optical imaging, myocyte isolation, cell culture, and molecular biology. Expertise in electrophysiology and imaging approaches is highly preferred