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solutions and policy impacts. You will design and implement machine-learning algorithms that interact with your simulation framework for scenario discovery, building surrogate models of simulation outputs
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background within (at least one, preferably several of) the following fields: Biomedical circuits and systems Physiological Modelling Medical sensor and instrumentation technology Health Informatics Digital
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, generative design, building performance optimization, digital design methods (e.g., predictive modeling, multi-agent systems and algorithmic techniques for architectural design), digital design epistemologies
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, utilizing cutting-edge reconfigurable computing and sensors. The aim is to develop next-generation electronics and computing solutions that solve technology-related challenges in fields such as autonomous
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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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algorithm. Design methods: Develop novel control methods for power electronic converters feeding electric machine Simulation: Learn advanced simulation tools such as Ansys to simulate and analyze the effect
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-based topology optimisation and de-homogenisation Adaptive meshing algorithms for topology optimization PDE-driven topology optimisation methods Research fund application Collaboration with industrial
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based on both visual and tactile information. The candidate will be responsible for developing detailed simulation models of both robots, sensors, and components to be assembled. In addition