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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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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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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