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of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key goals include optimising convective heat transfer
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the development of a phase-field model to study the mechanisms of formation and growth of tetrataenite (Ni-Fe with the L1₀ ordered structure) during cooling. The objective is to incorporate the presence
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microenvironment, and other biological processes. Conduct preclinical studies using in vivo disease models (cancer, inflammation, organ injury, etc.) and new alternative methodologies (advanced biological models
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-equilibrium conditions. The project is a UKRI/NSF collaboration with Virginia Tech, and the use of direct numerical simulation, modelling and analysis will be complemented with experimental data from
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comprise all aspects of applied mathematics, from model development and mathematical analysis to numerical implementation and statistical estimation of models as well as software packaging. What to expect
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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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, physical modelling experiments, field investigations, and/or numerical modelling. The specific focus area for the research can utilize any of these tools, or their combinations, and will depend
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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
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The Department of Biostatistics & Bioinformatics at Duke University invite applications for a postdoctoral associate position in mathematical modeling, as part of a new NIH-funded Center for Excellence in
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as instructed. Purpose As a part of a multi-disciplinary, integrated research team, the candidate will participate directly in efforts to ground deformation assessment and numerical modeling of ground