14 analog-and-integrated-circuits Postdoctoral positions at Chalmers University of Technology
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We invite applications for several postdoctoral research positions in experimental quantum computing with superconducting circuits. You will work in the stimulating research environment
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superconducting circuits. A part of the project is addressing specific challenges related to this goal. One such challenge is to efficiently read out quantum information from qubits. To achieve this, a first-stage
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initiative to develop a quantum computer based on superconducting circuits. Part of the project is also to solve specific problems related to this goal. A key challenge in this effort is mitigating errors in
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particles will be assembled into larger one-dimensional and three-dimensional structures and integrated with other material systems. Characterization techniques will include electron microscopy, X-ray-based
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center for catalysis at Chalmers. The goal of the center is to develop catalytic technology for sustainable transport, energy, and environmental systems. The center is integrated with the research and
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-, and fuel-related Risk Influence Factors (RIFs). Translate potential risk scenarios into Unity-based extended reality (XR) modules and embed proactive Risk Control Options (RCOs). Integrate developed
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within research and education. The following will strengthen your application: Familiarity with any of: molecular simulation methods (integrators, force fields, enhanced sampling), structural biology
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-based prediction and design of new, low-dimensional magnetoelectric materials. Low-dimensional materials are highly desirable for efficient device integration, and the intrinsic coupling between electric
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datasets. By integrating data-driven insights with innovative modeling, this interdisciplinary project aims to enhance our understanding of vulnerability, resilience, and adaptation, ultimately informing
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large