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PhD in Advanced Testing, Modelling, and Simulation of Composite Materials under High Velocity Impact
work. Some experience in modelling of composite materials. Some experience in programming (Python, Fortran, CUDA or similar). Some experience in fluid mechanics and biomechanics. Personal characteristics
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learning, computing, data science, biomedical engineering, or a related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with
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deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. You have good programming skills in Python and experience with a deep learning framework such as PyTorch
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evidence of excellent study results. You have strong programming skills in C and at least one high-level language (e.g. Python). You have a solid understanding of wireless network stacks and/or experience
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wireless system. Analyse experimental measurements using MATLAB, Python or equivalent computational tools. Compare experimental measurements with modelling or theoretical predictions generated by the wider
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skills. experience in data analysis, quantitative modeling and programming (e.g., R, python); knowledge of nutrient and/or agrochemical cycles in agriculture; excellent scientific writing skills in English
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interest in the human brain. Programming experience (Python, MATLAB) and proficiency in spoken and written English is required. Experience with or an interest in microscopy, quantitative image analysis
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, specifically with Python-based engineering software development and hardware interfacing. Excellent oral and written English skills Demonstrated ability to independently conduct engineering projects Demonstrated
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. Solid programming skills (Python preferred; experience with PyPSA, MATPOWER, or similar modeling tools is a strong advantage). Reflective, analytical approach with aspirations to publish in high-impact
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perception, optimization, or control will be an advantage. The candidate should be comfortable with scientific programming, for example in Python and common machine-learning frameworks such as PyTorch