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at the master’s level, strong programming skills in Python, and experience with at least one of the popular deep learning libraries (PyTorch, TensorFlow, Keras, etc.). Good verbal and written communication skills
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, multiphase flows, or similar areas. A deep understanding of the physics and dynamics of fluid and thermal systems. Proficiency in programming and data analysis tools, such as Python and MATLAB (or equivalent
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on the following assessment criteria: Knowledge in energy technology, large language models (LLMs), deep learning, and Python programming. Meritorious qualifications include knowledge in power engineering, power
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programming skills in Python are necessary, along with an interest in combining physics with modern computational tools. Good knowledge SQLite is beneficial. Excellent ability to communicate in English, both
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modeling reaction kinetics. Proficiency in Python programming. Application procedure The application should be written in English be attached as PDF-files, as below. Maximum size for each file is 40 MB
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Python programming. Application procedure The application should be written in English be attached as PDF-files, as below. Maximum size for each file is 40 MB. Please note that the system does not support
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assessment criteria: Knowledge in energy technology, large language models (LLMs), deep learning, and Python programming. Meritorious qualifications include knowledge in power engineering, power electronics
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system modelling. Solid knowledge in mathematics, physics, thermodynamics, energy technology, optimization, and programming for system modelling, with experience in tools such as Matlab, or Python
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knowledge in programming (preferably in Python), personal characteristics, such as a creativity, thoroughness, and/or a structured approach to problem-solving are essential. Additional qualifications
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such as Matlab, or Python. Excellent command of spoken and written English. Additional qualifications Experience with modelling, simulation, and optimization of energy systems. Experience in thermodynamic