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. Experience in scientific programming (e.g., Matlab and Python) is a requirement. Experience with analysis of climate data sets is an advantage. Applicants must be able to work independently and in a structured
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cycle processes, dynamics of oxygen and nutrient cycles, is required. Expertise in scientific scripting, programming, and data analysis (e.g., Python, Matlab, R) is required. Knowledge of climate
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01.07.2025. It is a condition of employment that the master has been awarded before employment. Documented knowledge on renewable energy is required. Experience and proven knowledge in scientific programming
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. The position is for a fixed term of 3 years and is associated with the LEAD AI project co-funded by European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant
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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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-based drug design. The position is for a fixed term of three years and is associated with the LEAD AI project co-funded by European Union’s Horizon Europe research and innovation programme under the Marie
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project itself. The remaining six months will be allocated to formal training. As a PhD candidate, you must participate in the Faculty of Humanities’ PhD programme . The PhD programme comprises a training
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candidate, you must participate in the Faculty of Humanities’ PhD programme . The PhD programme comprises a training component corresponding to 30 ECTS, which corresponds to one semester. Qualifications and
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mortality. About the LEAD AI fellowship programme LEAD AI is the University of Bergen's career and mobility fellowship program for training 19 postdoctoral fellows in artificial intelligence. The program has
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. The proposal should contain a progress plan for the different parts of the research work, as well as a plan regarding publishing. It is a requirement that the project is completed in the course of the period of