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this position, you will develop high-fidelity block-based numerical models capable of representing the geometric and mechanical complexity of historical multi-wythe masonry. Your work will involve analysing how
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motivated graduate looking to apply your skills and curiosity to create a greener transportation industry. With your pioneering mindset and communicative nature, you thrive in complex multi-disciplinary teams
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multi-disciplinary project. You also possess: a successfully completed MSc degree (120 ECTS points) in Human Nutrition, Food Science, or a closely related field; strong quantitative skills (e.g. R, Python
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Center). We anticipate one of the cluster hires to have future Dutch-teaching responsibilities. Ability to start the position July 2026 or as soon as possible thereafter. Employment conditions and benefits
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Training Center). We anticipate one of the cluster hires to have future Dutch-teaching responsibilities. Ability to start the position July 2026 or as soon as possible thereafter. Employment conditions and
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of the position. About the project: NO-REGRETS is a 5.5-year, multi-disciplinary research program co-designed with stakeholders to understand the ecological and economic tradeoffs of upscaling offshore wind farms
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representation, regulation, and access intersect. Through multi-sited qualitative ethnography at selected media-related and popular-culture heritage sites across Europe and its overseas countries and territories
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organizational skills, with the ability to plan, coordinate, and execute a multi-year research project Strong communication and collaboration skills, and a clear willingness to work in a team High proficiency in
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Offices, ensuring synergy, a consistent approach, the sharing of experience and confidentiality. leading and managing the Department’s cost centre, resources and competencies, including multi-year resource
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-cell and bulk transcriptomics, mutant screens and transformation approaches to identify signalling pathways and regulatory components. You will analyze data and integrate multi-modal datasets by applying