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of the following topics will be a plus: Application of machine learning to power grids/cyber-physical systems Knowledge of working with MATLAB/Python Power grid optimisation/control Experience working with power
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, training, and collaboration Preferably a strong background in aircraft design and propulsion systems Preferably Proficient in programming (MATLAB preferred; Python is also acceptable) Prior experience with
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in one or more of the following areas: Organic synthesis Polymer chemistry Computer programming (Python) Submitting an application As well as supporting documents, applications must include a Research
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modeling and data analysis. An interest in groundwater contamination, risk assessment, and sustainability. Programming experience (Python, MATLAB, or similar) is desirable but not essential. However
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, or the equivalent qualifications gained outside the UK. Experience in coding in both Python and R is essential.
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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc
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modelling and simulation techniques and software packages would be an advantage. Programming skills in languages such as Python, C++, MATLAB, are desirable, as is an awareness of machine learning or other AI
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the coupled aerodynamic, structural and thermal analysis of interface regions, for example between fins and fuselage, in hypersonic vehicles. This will involve the use and augmentation of in-house python-based
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Engineering, Physics or Materials Science Excellent English written and spoken communication skills The following skills are also highly desirable: Ability to program in Matlab, Python or similar Strong
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highly desirable: Ability to program in Matlab, Python or similar Strong laboratory-based skills Application Procedure Informal enquiries are encouraged and should be addressed to Prof Daniel Eakins