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critical, to ensure expected engine performance is achieved. To predict this complex flow and heat transfer, next-generation Computational Fluid Dynamics (CFD) solvers using Large-Eddy Simulation (LES) and
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essential for identifying ecological tipping points and forecasting climate change impacts in complex marine ecosystems. This project will apply a multi-level approach (molecular, physiology and ecology
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Skills: Research-driven and self-motivated, with a strong passion for energy system related studies. Proficiency in English with strong communication and scientific writing skills. Competency in computer
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Research at the LMB aims to understand the workings of complex systems, such as the immune system and the brain, and use this information to solve key problems in human health and disease
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
, computer vision, and data analysis using industry-standard tools such as Python, MATLAB, and deep learning frameworks. The student will enhance their ability to manage complex, interdisciplinary research
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involve experimental optimisation, leveraging computational tools, statistical modelling, and emerging AI/ML applications to streamline and accelerate the workflow for complex mixtures and metabolomics
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effects on the human host, either beneficially, such as antibacterial compounds, or negatively, such as toxins. Computational analysis of genomic data highlights a vast number of pathways to such molecules
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Deadline: 7th January 2026 One fully funded, full-time PhD position to work with Dr. Viacheslav Borovitskiy in his new research group at the School of Informatics, University of Edinburgh. Our
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models that represent and reason about complex biological systems, enabling predictions and interventions that can alter system behaviour in desired ways. For example, why do cells respond differently
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habitat fragmentation. Working at the forefront of ecological modelling and movement ecology, you will build next-generation, process-based models to predict how real populations respond to complex