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important for renewable energy production and production variability will be an advantage. Knowledge of machine learning or optimization will be an advantage. Applicants must be able to work independently and
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. Knowledge of machine learning or optimization will be an advantage. Applicants must be able to work independently and in a structured manner and demonstrate good collaborative skills. Applicants must be
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. Experience in programming in languages such as MATLAB, R or Python is a requirement. Experience in Magnetic Resonance (NMR, MRS, or MRI) is a requirement. Experience in machine learning and processing
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. Experience in machine learning and processing of experimental data is an advantage. Applicants must be able to work independently and in a structured manner and demonstrate good collaborative skills
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UNIX/Linux interface and basic programming (e.g. Python) is a requirement. Experience with machine learning is an advantage. Experience from free energy calculations is an advantage. Applicants must be
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project/work tasks: The SnowAI project aims to use to produce new high-resolution datasets on snow depth in Western Norway derived from machine learning and radar remote sensing. The successful PhD
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. Conceptually, this includes data from a single experiment (regularization), across two experiments (registration), and for analyzing large datasets (statistical analysis and machine learning). This development
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synthetic steps (and routes), using machine learning in closed-loop (autonomous) optimizations. The tasks will be varied, and you will have the opportunity to contribute to developing new solutions in a
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at https://cbu.w.uib.no/joshi-group/ . Co-supervisors include experts in machine learning and AI, Pekka Parviainen and Tom Michoel, alongside leading epidemiologists, Tone Bjørge and Kari Klungsøyr. The core
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be employed by any other institution for the time of the fellowship. Experience with AI-related research and/or innovation is an advantage. Experience in machine learning is a requirement. Experience