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structural dynamics, hydrodynamics, or marine systems Experience with programming or numerical tools (MATLAB, Python) A willingness to work across disciplinary boundaries Motivation to tackle open-ended, real
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for admission to a PhD programme. Strong background in Computational Mechanics, Solid Mechanics and Numerical Methods. Knowledge of the Finite Element Method. Programming skills (Python & Fortran). Good written
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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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data analysis Strong foundation in mathematics Strong programming skills in Python (Python Data Stack, PyTorch, etc.). Good oral and written presentation skills in Norwegian/Scandinavian language
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; Experience in handling spatial data; Programming experience in Python, and/or R; Proficiency in English; and The ability to work as part of an interdisciplinary research team. The following qualifications
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audiences and collaborate across disciplines; proficiency in spoken and written English. Experience with plant pathogens, microbiome sequencing, image-based phenotyping, R/Python, statistics or machine
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numerical experiments; programming experience in a language such as Python, Julia, MATLAB, C or C++; good written and spoken English; the ability to work independently while also collaborating in
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, or Python. The studentship covers fees at Home rate (UK and EU applicants with pre-settled/settled status and meet the residency criteria). International applicants must cover the difference between Home and
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and/or Python; experience with Unix/Linux environments and bash is considered an advantage Prior experience in the preprocessing, analysis and interpretation of omics data and a strong interest in
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. The appointment is to be made in accordance with NTNUs guidelines for recruitment positions for general criteria for the position. Preferred selection criteria Experience with computer coding, such as R, Python