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Field
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selection criteria Experience with behavioural-related experimental work Knowledge on programming in R/Python is beneficial Personal characteristics To complete a doctoral degree (PhD), it is important that
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an advantage: Natural Language Processing; LLMs; R; Python. Experience with teaching and supervision will be considered an advantage. Very good skills in Norwegian or other Scandinavian language is an advantage
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processing, software-defined-radio and similar A solid background in programming and signal processing (C, Matlab, Python, …) Knowledge and experience with embedded systems are a strong plus (C, FPGA
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interest in translational endocrinology and digital health technologies Basic programming or data science skills (R, Python) and interest in wearable data analysis are an asset Excellent command of written
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analysis workflows (Python and/or Julia-based; HPC-oriented handling of large datasets). Depending on competence: contributing to research software development supporting simulations and/or data workflows
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mathematics). You must have solid programming skills and documented experience implementing algorithms and running reproducible experiments in Python and/or MATLAB. PLEASE NOTE: For detailed information about
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mathematics). You must have solid programming skills and documented experience implementing algorithms and running reproducible experiments in Python and/or MATLAB. PLEASE NOTE: For detailed information about
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, e.g., the Journal of Fluid Mechanics, Combustion and Flame, Proceedings of the Combustion Institute. Proficient in MATLAB and/or Python. Strong written and oral English language skills. Strong written
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. Appointment is dependent on the public defense of the doctoral thesis being approved. Experience with analysis of time-series of neural data Proficiency in programming custom analysis pipelines (e.g. Python
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degree has been awarded. A good understanding of ocean and/or climate dynamics is a requirement. Proficiency in scientific programming (e.g., MATLAB or Python) is a requirement. Experience in statistical