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the analysis of large-scale omics data (e.g., proteomics, metabolomics, transcriptomics), including basic programming skills in R and/or Python Documented experience with exercise testing (e.g., testing
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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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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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; 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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, 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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, fluid mechanics, simulation, and programming (Fortran, C/C++, Python, …). Proficiency in English is a requirement. The position adheres to the European policy of balanced ethnicity, age and gender