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Field
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and 2). These measurements will then be incorporated into a GIS and compared to historical records of the position of channel networks prior to reclamation, such as aerial photography, to validate
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this ecosystem service. The student will map pedestrian networks using GIS and combine this with existing vegetation data. Field surveys will be conducted to validate the spatial data by measuring shade and
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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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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 skills (in Python, R or similar) and experience with large-scale data analysis using bioinformatic tools and pipelines to analyse and interpret biological data Attributes and Behaviour • Be
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with programming (Python, MATLAB), background in aerospace, computer science, robotics, or electrical engineering graduates, hands on skills in implementation of fusion/learning based techniques in
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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