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Field
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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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(e.g. C++, Python, Matlab) OpenFOAM (beneficial, not required) Strong analytical and problem-solving skills. Approval and Enrolment The scholarship for the PhD degree is subject to academic approval, and
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and hands-on experience with AI and computer vision. Solid programming skills in Python, especially with PyTorch. Practical experience with deep learning projects, including working with attention
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of supervisors that covers both academic pursuits. Primary tools are the programming language Python to exploit its advanced deep learning toolboxes (e.g., Keras, TensorFlow, and PyTorch), alongside
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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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for data analysis and experiment automation (Python preferred) Excellent English communication skills (written and verbal) Demonstrated ability to work in interdisciplinary and collaborative environments
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communication, or related domains. Proficiency in programming (C/C++, python) is necessary for this position. It will be considered advantageous if you also have experience in mobile robotics, and/or machine
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analysis and analytical data analysis workflows, together with other team members Implementing AI-based microscopy image analysis software as python packages Developing algorithms to deploy machine learning