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on the topic (2,4). Training and Development Training will maximise future employability in academia and industry: Programming and geospatial data analysis using Python/R. Machine/deep learning techniques
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. Candidates must have proven ability to work with large datasets, coding with Python/Fortran/C++ and ideally experience with high-performance computing. Applicants from an industry background are encouraged
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, Psychology, or a related field, to be awarded before March 1st, 2026. Essential skills include an ability to code (e.g., Python, R) and interpret data, knowledge of machine learning and statistics, and a
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related subject Previous coding experience (e.g. python, bash, matlab) Good administrative skills, and the ability to organise and prioritise workload Excellent interpersonal, oral and written communication
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: At least an upper second-class degree (preferably MSc) in a Science or Technology discipline. Good working knowledge of machine learning and deep learning. Hands-on knowledge of Python or PyTorch
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. The following skills are highly desirable but not essential: Ability to program in Matlab/Python Experience with Finite Element Analysis and Reduce Order Modelling Experience in Rapid Prototyping and CAD Design
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: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in implementation of fusion
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. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in 2D convolutional neural networks in Python. This is a part-time position (5
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highly desirable, alongside knowledge of MATLAB/Simulink/Python or similar tools. Familiarity with sustainability frameworks (e.g., ESG, Mission Life-cycle Assessment) and interest in interdisciplinary
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2D convolutional neural networks in Python. This is a part-time position (5 hours/week) funded until 31/03/2026 with a possibility of extension and is suitable for a Ph.D. student with relevant