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discipline with a focus on integrated circuit design, packaging, and EDA (electronic design automation). Solid understanding of the IC physical design flow, familiarity with commercial EDA tools (e.g., Cadence
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trials based on experimental design. Contribute to the numerical modeling of beneficiation circuits (e.g., using USIMPAC or similar software). Analyze, interpret, and report the outcomes of simulations and
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Qualifications: Experience with synthetic biology approaches and circuit design. Background in immunology, particularly T cell biology or CAR-T cell engineering. Experience with in vivo mouse models, especially
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superconducting qubits and millikelvin electronics Did you recently get your PhD in circuit quantum electrodynamics (cQED) and are now looking into taking the full potential of your skills into use for making new
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on developing TFLN and TFLT photonic integrated circuit (PIC) platforms for classical and quantum information processing. Our approach involves cavity-based components with electrically controllable cavity
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candidate will: ● Conduct independent research in electrical engineering with applications in robust and efficient AI systems; design and develop circuits, methodologies, research experiments aligned with
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for deep access within the human body. The research remit will encompass: mechanical and electronic circuit design, prototyping, and pre-clinical evaluation of diagnostic and therapeutic devices to tackle
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to join our group. The candidates will join an ambitious international research and innovation project focused on silicon quantum photonics. The overarching goal of this newly funded project is to realize
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key responsibilities will include: Designing and implementing advanced LabVIEW and C++ based control software for our HS-DAFM platform Developing specialized signal processing algorithms and circuits
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continuous-variable quantum computing using 3D cluster states and hybrid (photon number + quadrature) detection. TopQC2X (Innovation Fund Denmark): Experimental primitives for topological quantum computing