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relevant discipline in engineering, science, or mathematics. Experience with modelling, simulation, optimisation, or programming (e.g. Python, MATLAB, C++, or similar) would be advantageous, though not
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remains a challenge. This project will develop humidity-controlled terahertz spectroscopy to probe water properties within membranes, advancing material insights to optimise trade-offs for next-generation
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analysis. • Hydrological and hydraulic simulation. • Machine learning, including unsupervised clustering and predictive modelling. • Working with large, complex, multi-source datasets using MATLAB, Python
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. Programming experience (e.g. Python, MATLAB, C/C++). Desirable (but not required): Background in control theory, dynamical systems, optimisation, or machine learning. Experience with robotics, ROS, simulation
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of shaping, steering, and optimising wave propagation far beyond what is possible with conventional antenna arrays. This PhD project contributes to the ENACT project on environment-aware communication modes
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data-density reaction/bioanalysis techniques, including high-throughput experimentation, to inform and enhance drug optimisation. Employ machine learning to analyse complex datasets, extract meaningful
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. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Interviews for this studentship
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in polymer, composites, or soft-matter modelling, scientific programming, and machine-learning-supported optimisation is an advantage. PhD Position 3 – Computational–Experimental Integration This PhD
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an allied field. An MSc degree in a relevant area is desirable though not necessary. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning
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topologies and experimentally characterise their magnetic, mechanical and thermal performance. The optimised design for manufacturing workflow will be demonstrated on application-relevant prototypes