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complex anatomical structures such as blood vessels, the bronchial tree, and endoscopic pathways. Despite their potential, challenges remain in accurate modeling, sensing, and control due to their highly
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focus on physical and mathematical aspects is required. In addition, scientific programming skills (preferably in Python) and experience with numerical models are essential. Desirable qualifications
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. Experience with isotopic box modeling, numerical simulations, and statistical analysis. Demonstration of effective communication of scientific and technical results (e.g., peer-reviewed publications
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Description Your Responsibilities Participation in one or more research fields of the institute: Material modelling of e.g. fresh concrete, soil, biological tissue (aorta, skeletal muscles) Numerical
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of how alternative land management practices impact greenhouse-gas fluxes through the development and application of sophisticated modelling tools. The work will involve model development on the Cambridge
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transport, microbial systems, or circular bioprocesses. You will contribute to developing and applying novel modeling strategies, AI-enhanced simulations, and computational workflows to explore biological
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associated numerical methods and AI, will be used with High Performance Computing (HPC) to improve understanding of key flow physics and inform future HPT design. Skills and Experience Required: Applicants
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King's College London Department of Engineering | London, England | United Kingdom | about 1 month ago
arising from different vegetation fire types such as crown fires, shrub fires, and smouldering fires. A methodology to link lab-scale and field-scale fires. Numerical model of ignition with a database of
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-scale electrode modelling and numerical upscaling techniques to the continuum scale will be applied. Finally, the aim is to develop a cell scale model using a macro-homogeneous description of the porous
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skills. Preferred Qualifications: Prior research experience in computational biology, mathematical modeling, or immunology. Familiarity with numerical methods, parameter estimation, and data visualization