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independently and collaboratively Structured and able to manage complex projects Strong in analytical thinking and problem solving Communicative and able to work across disciplines Jarli & Jordan / UiO via
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project will focus on reinforcement learning methods for generating complex structures with two possible application areas (i) the generation of virus capsids for gene therapy and (ii) the generation
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redistribution of snow across catchment and regional scale areas. The PhD candidate is further expected to quantify, through a combination of in-situ observations and climate and snow models of varying complexity
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on peptide-membrane interactions, and in particular, towards characterizing and understanding the dynamical and structural effects of various peptides on both simplified and complex membrane systems
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the Norwegian Prescribed Drug Registry and the Norwegian Patient Registry, we will track complex drug utilization patterns and mental health trajectories over time. In this project, we will also link
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of complex phenomena arising from applications both in the natural and social sciences. This includes research within risk and control methods from finance and insurance as well as biology or engineering. The
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uncertainty in AI systems operating on complex, heterogeneous data. Contributing to open, reproducible research practices within CAIR. A prerequisite for employment is that the candidate is to be admitted
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complex analysis skills for industrial problem scenarios. This will enable publications at prestigious conferences and in scientific journals. Therefore, this position opens up great opportunities for a
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comfortable communicating complex ideas clearly across disciplines. A collaborative spirit, a deep sense of responsibility for high-quality research, and a willingness to contribute to an inclusive and
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are key to making sound decisions. The subsurface is highly complex, and insufficient subsurface understanding is the main obstacle for obtaining reliable forecasts. Assimilating observed data