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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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The section focuses on conducting research in digital and high-frequency electronics, embedded systems, control, and communication, utilizing cutting-edge reconfigurable computing and sensors. The aim is to
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maintenance test facility at SDU. This includes dynamical simulation of breeding blanket extraction with a robot, design and implementation of real-time control system, selection of sensors and actuators
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limited to: Testing underwater frames and instrumentation, including sensor testing and calibration Quantifying benthic biogeochemical fluxes and momentum fluxes in situ from high-density datasets (e.g
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implementing AI algorithms to control robot systems Investigating and implementing Bayesian ML methods to quantify decision-making uncertainty Applying the above to industrial assembly and medical diagnostic use
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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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construction machinery to improve efficiency, adaptability, and safety under varying operating conditions. The work will involve designing and prototyping intelligent control algorithms, developing runtime
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our team, we are looking for candidates with experience in two or more of the following fields: Integration of sensors and actuators into complex mechanical devices Design of mechatronic products