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working memory contents held within. Second, we will use computational spiking-neuron models to explain the results of the experiments and implement the neural mechanisms responsible. These models will also
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dissecting RNA structure ensembles in living cells. Particularly, you will: Develop novel methods (both wet and computational) for the accurate interrogation of RNA 2D and 3D structures in living cells. Apply
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actors involved in managing public space? How can they adapt to dynamic societal needs and increasing spatial interdependencies? What institutional models promote more efficient, equitable and resilient
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of “the countryside” for both urban and rural residents. The PhD position is part of the NWO-funded research programme Fertile Soils, which conducts inter- and trans-disciplinary research into making relationships
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described in the project overview. Owing to the current composition of the project team, there will be a mild preference for candidates opting for project 2 on “Models and machine learning”. An explanation
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reconstruct the structural models, with an emphasis on picking and combining the right techniques, as well as quantifying the uniqueness of information obtained. Position Overview: You will develop an in-house
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you like to contribute to an increased understanding of carbon cycle feedbacks in the climate system? Do you thrive in the dynamic blend of seagoing fieldwork, laboratory experiments and modelling? As a
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, applying advanced modeling techniques and analyzing large-scale datasets to contribute to healthcare policy decisions. The PhD project will be supervised by Dr. Nora Franzen and Professor Maarten J. IJzerman
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The Groningen Institute for Evolutionary Life Sciences (GELIFES - https://www.rug.nl/research/gelifes/ ) offers a 4-year M20 Program funded PhD position for a project on “Multi-dimensional
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models. Requirements The successful applicants will have: A solid computational background, an interest in cognitive neuroscience a and strong deep learning programming skills. Ability to work in an