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data Design algorithms for correlating low-level events into process-level attack models Contribute to joint framework development with TU/e on continual learning Collaborate with industry partners
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modeling tools and HDL simulators to validate functionality. Collaborate closely with algorithm designers to co-optimize architecture. Publish results in high-impact journals and conferences. Qualifications
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that support spike-based processing and memory-efficient computation using SSMs, targeting edge-AI scenarios in wearables, robotics, or sensor networks. Research area and project description The project will co
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these frameworks to develop specific formulations and solution algorithms for the design of congestion pricing schemes using classical transport models and quantify the equity-efficiency trade-offs for congestion
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of prior data?) Additional research topics may include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success
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include: CMOS-based neuron and synapse circuit design Low-power digital architecture for SNN processing On-chip learning mechanisms Integration with sensor interfaces for biomedical signal processing What
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create metadata protocols to ensure consistent, reliable input for AI models. Design machine learning workflows to interpret large datasets, efficiently select algorithms, train and validate models, and