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. Where to apply Website https://www.academictransfer.com/en/jobs/363203/phd-candidate-treatment-optimis… Requirements Specific Requirements Profile We are looking for a highly motivated candidate with: A
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Website https://www.academictransfer.com/en/jobs/362752/phd-position-modelling-and-opti… Requirements Additional Information Website for additional job details https://www.academictransfer.com/362752/ Work
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focus We welcome candidates with a strong interest in developmental biology, organoid technology, and disease modelling. This position is part of the EU-funded Marie Curie doctoral network (VISI-ON-BRAIN
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rules directly from multicellular migration experiments. We therefore seek a candidate who is excited by rigorous statistical mechanics and data-driven modelling, and who is eager to use these approaches
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computational disciplines, gaining exposure to cutting-edge cardiovascular research and advanced cellular models. What you do Your research focuses on understanding how adipose tissue-derived signals contribute
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—including structured households’ surveys and Agent-Based Modeling (ABM)—for the quantification of adaptation effectiveness and limits. The overall outcomes of the ADELE project will contribute to the ongoing
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data, a physics-based train–track–embankment interaction model will be developed using finite element (FE) and/or multibody dynamics (MBD) approaches. The model will be used to investigate the mechanisms
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like to contribute to an ambitious research project combining behavioural studies, fMRI and computational modelling? If so, then we invite you to apply for this PhD position in the Cognitive Neuroscience
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learning, Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Your project is part of the DUAL-IMPACT project, which focuses on the development of new data-driven
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hiring a doctoral candidate on the subject "Foundation AI models for distribution systems decision-making ". Foundation models have recently emerged as a new learning paradigm in AI. These models learn