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models tailored to Dutch industry needs, focusing on underserved modalities (e.g. sensor data, time series, multimodal signals) and edge applications. The consortium develops an open-source toolbox
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industrial partners. Its aim is to develop and apply foundation models tailored to Dutch industry needs, focusing on underserved modalities (e.g. sensor data, time series, multimodal signals) and edge
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MobiMaestro, a multimodal traffic-management platform adopted by almost every major Dutch municipality and several provinces. MobiMaestro fuses real-time data from roadside sensors, connected-vehicle feeds
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optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware
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., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware optimizations (e.g., automated pipelining). The PhD student will be supervised by Nusa Zidaric. Key
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limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data
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interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data mining has allowed us to obtain insights from large amounts of data for decades, and it is worth
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optimisation algorithms to optimize the designs. We now hire three PhD candidates who be based at LIACS (Leiden University) and spend several months with industry and academic partners abroad. The GenAIDE
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of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data