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models for corneal regeneration through state-of-the-art (single-cell) multi-omics analysis. You will collaborate with other doctoral candidates in STEM-CORE through joint projects, secondments and network
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EMERGENCE and uncover how language emerges from large‑scale brain networks. Work with cutting‑edge neuroimaging and advanced modelling to reveal the neurobiological principles shaping human language. The ERC
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capture and collaborative governance in urban transformation processes, and assessing their social, environmental and economic impacts. You will develop a basic land valuation model to estimate land and
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(structural MRI, diffusion-weighted imaging, functional MRI), advanced connectomics, and receptor-enriched modelling to characterise individual differences in language organisation across healthy participants
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demonstrable interest and experience in probabilistic and/or other modelling approaches to sensorimotor control. You have good social, communication and organisational skills. You are a team player. Experience
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the project’s analytical framework. Co-develop and support research activities related to governance models, investment prioritisation, and EU alignment pathways. Assist in translating research findings
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. We now plan an intervention study for the upper primary grades in which we will address these topics, and which may, for instance, consist of eye-movement-modelling examples. As we are still in the
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with clinical populations or translational research settings. Strong programming skills (e.g. Python, MATLAB, R) and experience with statistical modelling or machine learning. A strong publication record
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research activities related to governance models, investment prioritisation, and EU alignment pathways. Assist in translating research findings into policy-relevant insights and advice. Draft policy reports
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populations or translational research settings. Strong programming skills (e.g. Python, MATLAB, R) and experience with statistical modelling or machine learning. A strong publication record and excellent