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technologies to solutions for the quantum-classical control and readout interface, and all the way to quantum algorithms and applications. The long-term mission of the programme is to develop fault-tolerant
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developing 3D vision algorithms for object detection, recognition, and scene understanding to support planning and task execution in dynamic environments. Publishing research findings in leading international
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behaviour. This will include developing and using state-of-the-art image recognition algorithms to create digital twin models as well as statistical and machine learning methods for analysing large-scale
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vision to reduce algorithmic complexity by orders of magnitude, e.g. by tracing paths of trees and extraction from knowledge bases (KBs), as opposed to pure DL Defining specific CSK-premises (in
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hardware modification. The AI will learn and adapt the realms of the combustion modes and fine tune the performance for each while the engine is operated. Self-tuning, adaptive, control algorithms will be
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will learn and adapt the realms of the combustion modes and fine tune the performance for each while the engine is operated. Self-tuning, adaptive, control algorithms will be used. This part of the three
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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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evaluate BCI algorithms for decoding motor intentions Integrate BCI systems with KAIST’s advanced exoskeletons Conduct experiments with healthy subjects and stroke patients Collaborate closely with a KAIST
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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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work at the intersection of palaeogenomics, bioinformatics, and evolutionary biology to overcome long-standing barriers in analysing degraded or low-quality DNA, enabling reliable genomic inference