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Field
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fully contactless power transfer that is free from wear and electrical arcing. We develop and implement complete stationary and dynamic IPT systems - from FEM simulation of the inductive transfer system
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of these. Be part of change Static and dynamic electrical characterization of power devices (Si and III-V semiconductors) at wafer level and in packages (from test structures to high-performance devices with
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national and international customers and project partners. What you contribute You have completed your diploma or master’s degree in the field of Electrical Engineering, Power Engineering, Control
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Gigabit Ethernet switch ICs, hardware design (PCB), and validation of the switch in the existing system What you contribute Advanced BA or MA studies in electrical engineering, electronics, or a related
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for detecting, characterizing, and mitigating oscillations, as well as analyzing control parameters What you contribute Pursuing a Master’s degree in electrical engineering, energy systems, power systems, or a
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the front end of line (FEoL) and back end of line (BEoL) of various CMOS technology nodes. Be part of change Electrical characterization & reliability analysis: You put integrated memory devices and ferroelectric
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grading scale) master's degree in electrical engineering and information technology, energy engineering, automotive engineering, computer science, mechanical engineering, industrial engineering, or a
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Semiconductor Laboratory of the Max Planck Society (HLL), Garching | Garching an der Alz, Bayern | Germany | 3 days ago
models and sensor designs. Contribute to scientific publications, technical documentation and intellectual property. Your profile PhD in physics, electrical engineering, applied physics, quantum
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technology—is offering the opportunity to write a thesis on the topic: »Real-Time Spectroscopic Monitoring of Microvias Powering the AI Revolution«. The explosive growth of artificial intelligence is driving
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Power grids are naturally represented as graphs, where buses, lines, transformers, generators, and loads interact through physical constraints. Recent developments in graph neural networks, and