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considered an advantage. Candidates should have programming skills in Python, R, or similar languages and be motivated to develop advanced modelling tools for sustainable transport planning. Your master’s
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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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Python, MATLAB, R or equivalent You must have relevant knowledge or experience in one or more of the following: railway engineering, infrastructure maintenance, rolling stock, condition monitoring, sensor
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be considered if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the NV faculty's Doctoral Programme . (https://www.ntnu.edu/nv
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deep learning, with a sound understanding of experimental research methodology documented through coursework, projects, or publications. You must have strong programming skills in Python and experience
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example through relevant work experience and/or peer-reviewed academic works. Experience in mechanical testing Experience with programming in Python or Matlab Strong analytical and problem-solving skills
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optimization. Strong programming skills (Python, Julia, Jax). Experience with numerical optimization is also desirable. Personal characteristics To complete a doctoral degree (PhD), it is important that you are
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. Demonstrated strong proficiency in programming (e.g., Python, PyTorch/JAX, or similar) and computational skills are required for this position. Candidates must have excellent interpersonal and communication
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essential for this position. A strong interest in probabilistic modelling, latent-variable methods or Bayesian methodology is essential. Demonstrated strong proficiency in programming (e.g., Python, PyTorch
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(Python, C++, etc.). Experience in mathematical programming and/or optimization software such Pyomo, GAMS or similar. Proficiency in process simulation software (Aspen Plus, Aspen HYSYS, etc.). Experience