30 high-performance-computing positions at Chalmers University of Technology in Sweden
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to increase catalyst activity and selectivity. The computational part of the project will investigate relevant reaction paths and evaluate spectroscopic signatures that can be compared to a parallel
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operation Quantum algorithm implementation and benchmarking About you You have a relevant Masters deegree corresponding to at least 240 higher education credits (Physics, Nanotechnology, Engineering, Computer
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, with researchers and students from around the globe. The Division of Marine Technology focuses on the professional understanding and technical development of ship operational performance. Research areas
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This postdoc project aims to address a critical challenge in quantum computing: errors in superconducting qubits caused by cosmic radiation, which cannot be corrected using existing methods
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to operate around the clock. By ensuring the performance, longevity, and circularity of industrial systems such as advanced manufacturing (e.g., automotive and battery) and renewable energy (e.g., energy
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to develop a more sustainable and energy efficient method for recycling carbon fibre composites. The approach uses alternating magnetic fields to heat the material in a controlled way, breaking down
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aim to develop a predictive model identifying advanced HF patients at high mortality risk within a year. Additionally, we plan an intuitive interface, utilizing large language models to communicate
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We are seeking two postdocs to collaborate on developing a near-quantum-limited microwave amplifier for readout in superconducting quantum computers. One position will focus on nanofabrication
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of waveforms for satellite communication and radar systems communication/radar system performance analysis using theory and simulation field tests in relevant operating conditions retrieval of geophysical
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these systems operate in, ACPS increasingly rely on data-driven learning-enabled components to perform a variety of challenging decision-making tasks. While indispensable for autonomy, learning-enabled components