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Field
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contribute to collaborative projects. Areas of expertise should include contrast-free super-resolution microvessel imaging algorithm, ultrafast ultrasound imaging system design, and hardware acceleration
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, Microelectronics, Computer Engineering, or a closely related field, completed by the start of the position Have a solid background in digital hardware design: Verilog/SystemVerilog RTL, logic synthesis, and place
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algorithms. model and optimize end-to-end physical-layer performance, hardware non-idealities, and overall power consumption. focus primarily on space and security topics (satellite communications, reliable
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circuit behaviour, and active device characteristics. Prior exposure to power amplifier design, load-pull techniques, or nonlinear/behavioural modelling is considered an asset. Experience with RF/microwave
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, control, and hardware solutions for converter-dominated power systems. Application areas include next-generation megawatt charging infrastructure for heavy-duty electric vehicles and power electronic
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designing and building hardware solutions, e.g. using Arduino, Raspberry Pi, 3D-printing, CAD and CNC machining and/or laser cutting Language Requirements: Excellent command of English (C1), both orally and
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problem. Do a theoretical analysis of the assigned problem using tools from information theory and related fields. Design a solution which is implementable on a computer and in hardware. Collaborate with
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SD-26109- POSTDOCTORAL RESEARCHER IN AI-BASED ENERGY MANAGEMENT OF RESILIENT MICROGRIDS WITH SECO...
Technology Organization (RTO) that drives innovation for the economy and society in Luxembourg and beyond. With cutting-edge expertise in Natural, Built, Industrial environments, Space, AI, Security and
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fields. Design a solution which is implementable on a computer and in hardware. Collaborate with colleagues to implement the solution in hardware at the ACES lab at the Institute of Theoretical
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for one fixed operating point and cannot adapt when grid conditions change. In this project you change how carbon-aware AI is designed. Instead of producing a single “best” model, you will use neural