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of the future! In this PhD position, you will design smart, adaptive end-effectors (robot grippers) and robotic systems that not only move, but also ‘think’ and feel. Using advanced sensors, precision mechanics
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further developing algorithms and writing an application to develop a wearable device functionality for monitoring patient behavior monitoring based on the gained insights. We value candidates who: have
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, Mechatronics, Electromechanics or similar) Proficiency in SolidWorks or similar CAD software Capability to design, develop and test complex mechatronic systems (concurrent design of mechanics, sensors
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manner, and (2) design AI approaches such as learning algorithms and reasoning engines that can exploit the provided knowledge? The successful candidate will conduct research on how to build software
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are actively engaged in the development of innovative methods and algorithms to be integrated in a digital twin architecture for (de) manufacturing systems. You contribute to organizing and performing
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at the KU Leuven, within the Biomed lab (https://biomed-kuleuven.web.app/research ) to develop new trustworthy AI algorithms for fracture detection based on novel photon counting CT technology. Particularly
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engineering or mathematical engineering Good understanding of statistics and machine/deep learning algorithms Interest in Biomedical data science Excellent programming skills in Python Proficient English, both
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to express complex domain knowledge in a formal yet flexible manner, and (2) design AI approaches such as learning algorithms and reasoning engines that can exploit the provided knowledge? The successful
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constraints across the FR3 band. You will propose novel transceiver architectures and communication algorithms offering flexibility and the best power-performance trade-offs. You will also investigate how
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-scaled CMOS models. Advanced algorithms and architectures need investigation of power-consumption trade-offs. For instance, digital predistortion (DPD) can enhance power amplifier efficiency but