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Field
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to work on building the next generation of quantum processors based on superconducting circuits. To achieve this ambitiuous goal we have a variety of projects related to: Simulation, design and
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nanostructured materials, with the aim to contribute both with fundamental understanding and practical applications such as dynamic metalenses or holograms. The detailed project will be designed together
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travel combustion engines. The research involves computational thermodynamics (CALPHAD), high-throughput computational screening methods for alloy design, experimental alloy production (casting and/or AM
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in clinically relevant environments. Key work assignments include: Design, fabrication, and optimization of high-performance plasmonic nanostructures and SERS substrates for sensing in complex
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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creating inclusive environments. Flexible and Supportive: Tailored training and career development designed to balance professional growth with personal commitments. State-ot-the-art Research: Engage in
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-throughput computational screening methods for alloy design, experimental alloy production (casting and/or AM), testing and characterisation of the thermo-physical and mechanical properties of the designed
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results demonstrate that compact heat-exchanger solutions—supported by conceptual design and aerodynamic optimization of integrated ducts—can deliver substantial reductions in specific fuel consumption and
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interdisciplinary approach often benefits from a broad computational toolkit; candidates who are interested in bridging these projects with insights from related fields—such as de novo protein design—to enhance our
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration