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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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quality comparable to that of experienced cardiologists. This PhD project centers on developing a framework for controlling a robotic arm equipped with a cardiac ultrasound probe and complementary sensors
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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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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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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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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