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Field
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scientific computing, for example Python Candidates without a master’s degree have until 1 October 2026 to complete the final exam. Desired qualifications: The following qualifications are considered strongly
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datasets metabolomics or analytical chemistry microbial community analysis programming/scripting in R, Python, or similar languages systems biology or computational biology Knowledge of a Scandinavian
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understanding of experimental research methodology documented through coursework, projects, or publications. You must have strong programming skills in Python and experience with a modern deep-learning framework
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the master's degree has been awarded Solid background in mathematics and physics is a requirement Experience in scientific programming (e.g., Matlab, Python) is requirement Experience in statistical analysis
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cookie and refresh page to watch video, or click here to open video) About the position This PhD project is connected to FME NorthWind (https://www.northwindresearch.no/ ), a Norwegian national research
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metrology, profilometry or microscopy, together with statistical analysis in R, Python or equivalent software. Dental crown materials, restorative dentistry, prosthodontics or dental laboratory workflows
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, structure preserving deep learning, stochastic differential equations, generative AI, numerical optimization. Strong programming skills (Python, Julia, Jax). Experience with numerical optimization is also
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for appointment. Fluent oral and written communication skills in English Experience with Solar Orbiter data analysis Programming skills in Python/IDL/Julia Desired qualifications: Experience with scaling and
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computing (e.g., Python, Matlab, C++ or similar) Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Enjoy working both independently and as part of
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meet the requirements for admission to the faculty's doctoral programme in Engineering Cybernetics . Strong programming skills, in particular Python, and practical experience with modern machine learning