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, nanofabrication, and computational electromagnetism. Strong coding (Python /MATLAB) and experimental aptitude is desirable.
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with coding, ideally in Python or MATLAB Funding support This studentship is open to Home students only. It is jointly supported by the Faculty of Engineering and industrial partners which is expected
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., health and climate/environmental data) and could include a range of data science methods, such as utilising geographical information systems (GIS), statistical analysis, machine learning, deep learning
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as Physical Geography, Geology, or Engineering Geology, with a numerical background in earth surface processes. Field experience and skills in GIS and programming skills are necessary. The scholarship
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or Abaqus (or willingness to learn) General knowledge of structural analysis and material behaviour, especially failure mechanisms Some experience with coding, ideally in Python or MATLAB Funding support This
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-field methods) Multiscale mechanics and microstructure-property relationships Python/C++/Matlab-based simulation and data analysis Industry-facing research and technology transfer You will also benefit
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, environmental data science, or a closely related STEM discipline Demonstrated expertise in urban spatial data analytics, with proficiency in GIS software (e.g. QGIS, ArcGIS) and geospatial methods Experience in
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will be an advantage if candidates have an interest in optimisation, control and computing systems, model checking, mathematical logic and good programming skills, ideally in MATLAB, Python and/or C/C
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training in field work techniques, ecological modelling and GIS will be provided by an interdisciplinary supervisor team. Funding duration – 4 years Funding Comment This scholarship covers the full cost
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). Experience in numerical modeling and data analysis. An interest in groundwater contamination, risk assessment, and sustainability. Programming experience (Python, MATLAB, or similar) is desirable but not