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Field
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scientific computing, to name a few. Modern LC applications rely heavily on accurate and efficient mathematical modelling of confined LC systems. Typical questions are - can we theoretically predict physically
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intensifying upper ocean mixing processes. However, major gaps in our understanding remain due to challenges in observing and modelling the Arctic Ocean. Research Methodology The aim of this project is to
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This PhD project is at the intersection of electromagnetism, numerical methods, and high-performance parallel computing, with application towards the design and optimisation of integrated circuits
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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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-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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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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-range of disciplines in future climate model development, paleo-climate data collection, and applied mathematics. Your qualities The project requires the development of both numerical skills and
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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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in a quantum microscope. Your tasks Planning and execution of optically-detected magnetic resonance experiments with diamond Analysis and modelling of the experimental data Carrying out literature
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observations and modelling of the physics and biogeochemistry of Antarctic shelf seas. You will gain experience in computer coding, statistics for environmental science, working with and piloting autonomous