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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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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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analysis systems, to ensure safe and trustworthy results. This can involve research questions from NLP and AI like model robustness and guardrails, human-computer interaction such as interpretability and
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procedures for yourself and, if applicable, for your family. If you come with a partner, we offer a Dual Career group program organized by our Welcome Center . This program helps your partner to get familiar
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diverse range of nationalities, and an active visitor program which attracts the best string theorists from all over the world. Our group's faculty members include: Jay Armas, Daniel Baumann, Jan de Boer
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training programme is part of the agreement. The indicative starting date is around January 2026. The position will be filled as soon as a qualified candidate has been found, so interested candidates
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to be expected. A PhD training programme is part of the agreement. The desired starting date is January 2026. The position will be filled as soon as a qualified candidate has been found, so interested
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PhD candidate in Design and synthesis of pyrophosphate mimetics to study enzymes involved in natural
freedom to assemble your own set of terms and conditions. For international spouses we have set up a dual career programme. Candidates from outside the Netherlands may be eligible for a substantial tax
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on sufficient progress in the first year to indicate that a successful completion of the PhD thesis within the next three years is to be expected. A PhD training programme is part of the agreement and the
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robotic systems and AI models. You will learn how to programme advanced robotic systems and how to implement aspects of deep learning and neural networks for chemical property prediction. You will be part