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degree in Engineering and have an interest in and/or a good understanding of numerical modelling and testing of structures. Prior knowledge of finite element methods and programming (e.g. C++, Python
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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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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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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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programming with Bash and Python is also required and previous experience of vertebrate and invertebrate genomes would be beneficial. The ideal candidate will be an independent researcher capable of managing a
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monitoring. Familiarity with tools such as Python, MATLAB, or embedded C would be advantageous. Most importantly, this project is ideal for applicants who are motivated to tackle real-world reliability