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formalizes the synergy between physics, information theory, and machine learning, particularly focusing on computing with Oscillatory Neural Networks (ONNs). Project The project aims to formalize the synergies
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within the Department of Electrical Engineering of Eindhoven University of Technology (TU/e) seeks to hire an outstanding PhD candidate or Postdoc within the field of high-voltage pulsed power research
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systems on random graphs by rigorously characterizing the set of Gibbs measures. Applications of such problems range from computational complexity, coding theory, to machine learning. This PhD project
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network and data centers, the transmission link optimization at the physical layer, and the computing decentralized systems by exploiting state of the art machine learning approaches to ultimately implement
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-edge research in the development of novel devices such as field effect, memristor and oscillator which contributes to enabling novel brain-inspired computing architectures for advanced machine learning
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on power systems and optimization/machine learning techniques. The position is also open to candidates at the final stage of their PhD who have not defended yet. Ability to conduct high quality
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process modeling (physics-based), ii) data-driving modeling (system identification, machine learning), iii) advanced process control and optimization. Demonstrated knowledge of using commercial process
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with hardware architecture, FPGA, and system-level simulation is desirable. A good theoretical understanding of statistics and machine learning theory. Strong analytical skills and proficiency in
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an accurate overview of the state of the art (intersection of urban planning, VR experiments, digital twining, big data and machine learning); as well as recent developments in this field of study in academics
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these limits. Main areas of interest are source coding, channel coding, multi-user information theory, security, and machine learning. We typically use information-theoretical frameworks to model the scenarios