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events and in machine learning for the earth system is required Strong and demonstrated programming skills are required Prior experience with geospatial data analysis in Python, working on scientific HPC
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. Demonstrated proficiency with scientific programming (e.g. Python) is an advantage. Demonstrated proficiency in geospatial data analysis is an advantage. Applicants must be able to work independently and in a
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contributing both fundamental methodological advances and practical decision-support tools. Research environment The PhD position is part of the Norwegian Centre on AI for Decisions (aiD), https://aid-centre.no
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to demonstrate skills on Data Analytics and Machine Learning, in particular on distributed ML. Must have very good programming competence in Python, Java, C/C++ or equivalent Fluent oral and written communication
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project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD fellow will be part of a growing
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Documented Python programming skills Desired qualifications: Experience with change point detection and anomaly detection Relevant experience with data from the maritime sector Language requirement: Good oral
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position This PhD project is connected to FME NorthWind (https
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to the faculty's Doctoral Programme (https://www.ntnu.no/studier/phma ). Applicants must have significant programming experience ideally in C, C++, R and/or Python. You must have good written and oral
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or real-world evidence generation. Strong programming skills and experience using statistical software and programming languages commonly applied in health data research, such as R, Python, SAS, SQL
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to the faculty's Doctoral Programme (https://www.ntnu.no/studier/phma ). You must have documented programming experience relevant to scientific computing, for example in Python, MATLAB, Julia, C++ or similar