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of activation. This project aims to close this gap by developing methods to identify malicious domains closer to their inception, as soon as indicators of compromise surface. Building on our prior work using
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1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD position 2 will focus on designing scalable
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, and machine capacity. Your PhD will address these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using
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of the future. SOURCE will deliver the scientific knowledge, models and design tools to develop and evaluate nourishment strategies in a multi-stakeholder co-creation process. Our Living Labs are two sand
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sensor topologies Development of the MEMS fabrication processes Fabrication of the sensor chips in the MESA+ cleanroom Evaluation of the resulting performance Development of the required interface
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a set of use cases with energy community projects ranging from smart grid experimentation and energy sharing in rural regions in the Netherlands to developing an energy community in Amsterdam
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aims to develop data-efficient and reliable training strategies for vision foundation models, reducing the need for large datasets and improving robustness. The project will explore strategies related
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of microtechnology, materials science, (stem)cell biology, and additive manufacturing. You will thereby be trained to develop a wide skill set, which will include tissue engineering, 3D bioprinting, bioink development
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. The program develops scientific knowledge, models and simulation tools to support marine spatial planning in a multi-stakeholder co-creation process. You will join a team of 17 PhDs and postdocs across
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1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD position 2 will focus on designing scalable