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Field
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master’s degree in (applied) mathematics or related fields - at least basic programming skills, such as Matlab/Python - good communication skills and interest in interdisciplinary research/geophysical
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system analysis or comparable Initial experience in optimization or statistics is an advantage Initial experience of an object-oriented programming language (e.g. Python, Matlab) and MS Office Analytical
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engineering, mathematical and programming contexts Your research will include extending and contributing to models and codes, including both high- and low-level programming languages, e.g. Python/Matlab
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, experience with programming (e.g., MATLAB, Python) is highly desirable Highly motivated, independent, and able to work effectively as part of a team Proficiency in English (both written and spoken
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experience with algorithms relevant to computational biology documented programming skills, e.g. in Python and R very good communication and organizational skills with the ability to work to timelines, both
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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 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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the development of theories, collaborate with the top experimentalists, and perform numerical simulations (matlab/python/mathematica). Key Requirements: • Hold a Master's degree (Bachelor's degree will also be
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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