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at international conferences and build a professional network across academia and industry. Development of expertise in cutting-edge experimental techniques, computational modelling, and interdisciplinary
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the evidence–base needed to understand the impact on health, to inform public policy, and to develop potential mitigation strategies. Traditionally, this information has come from ground monitoring networks
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and Advanced Computing Austria (ACA) — you will leverage state-of-the-art infrastructure, interdisciplinary know-how, and national networks. Your results will enhance the AI Factory’s Austria capability
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short courses in the core subjects of this PhD programme including process intensification and green chemistry. This project is part of the Process Industries: Net Zero (PINZ) Centre for Doctoral training
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the challenges of dynamic sensor networks for sleep management. Through the joint supervision between multiple disciplines, the student will be offered a unique opportunity to develop a robust personal portfolio
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represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster networking, enable internationalization and
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of healthcare by examining the impact of bottom-up communities and networks promoting change. Healthcare accounts for approximately 5% of the UKs total carbon emissions, and significant activity is underway
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of healthcare by examining the impact of bottom-up communities and networks promoting change. Healthcare accounts for approximately 5% of the UKs total carbon emissions, and significant activity is underway
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participates in the NWO research clusters GQT, STAR, NDNS+ and DIAMANT and in the Gravity programme NETWORKS. There is formal (and informal) cooperation with the Centrum Wiskunde & Informatica (CWI), the VU
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or infrastructure. This is what makes our daily work so meaningful and exciting. The Division of Computational Genomics and Systems Genetics is seeking from October 2025 a PhD Student in Deep Learning for Rare