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. The Biomedical Electromagnetic group focuses on developing new, more effective medical methods and systems for diagnostics and treatment. Core activities include signal processing, antenna design, and
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disease prognosis. This position offers a unique opportunity to work at the forefront of optical imaging technology, combining experimental optics with advanced computational and data-driven methods. Roles
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reconstruction methods (e.g., Born/Rytov approximations, multislice or multiple-scattering models). Proven experience in scientific programming and numerical computing (MATLAB, Python, or C/C++), including
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modern high performance computation facilities and parallel computing clusters (CPU and GPU). Excellent publication record and demonstrated conference presentation skills. Demonstrated ability to operate
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on competence: contributing to research software development supporting simulations and/or data workflows (HPC/parallel environments), and open/reproducible release of data and analysis scripts under FAIR
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for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics-aware learning methods with domain decomposition techniques, enabling parallel training and
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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evaluate numerical methods that combine low and high order formulations, and develop hybrid discretisation frameworks that apply varying fidelity across the computational domain. This includes creating
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Researcher (R2) Application Deadline 26 Mar 2026 - 22:59 (UTC) Country Sweden Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU