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universities, and three research and technology organizations—you will be at the forefront of shaping the future. This PhD project offers you the opportunity to develop cutting-edge competencies in digital
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. Qualifications You should have completed a two-year master's degree (120 ECTS points) in Civil, Architectural, Environmental, Electrical, Mechanical or Industrial Engineering, Autonomous Systems, Computer Science
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to correlate with improved generalization. Likewise, the behavior of neural networks is influenced by architectural choices and various training techniques applied during learning, known as inductive bias
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integration. The focus will be advanced packaging and integration mechanisms for wide-bandgap power devices and passive components (magnetics and capacitors), as well as innovative power delivery architectures
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to mutual agreement. We seek a motivated, curious, and outgoing PhD candidate with a solid background in fiber-optics, communication engineering and machine learning. Experience with application of machine
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surpassing that of state-of-the-art CMOS/SiGe BiCMOS based circuits. This should be achieved by exploiting innovative design techniques and architectures (e.g. gain-boosting strategies and loss-aware power
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Network” (AWETRAIN). Airborne Wind Energy (AWE) is a radical new technology based on tethered aircraft generating electricity from altitudes higher than conventional wind turbines. This allows