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amplifier performance. By combining advanced device measurements, empirical modeling, and power amplifier design, this project will generate new insights into the material, process, and design factors
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physiological balance. Perform LC/MS based proteomic analyses of circulating proteins and assess their impact on organs in mouse models and cell cultures. Analyze and interpret omics data using bioinformatic
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expression from text. Collaborate in multimodal data collection efforts, curating existing 2D and 3D datasets and leveraging our state-of-the-art performance capture studio. Conduct user studies evaluating
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compartments including single cell- or bulk sorted immune cells and extracellular vesicles from the lung of the patient cohort, as well as from cell culture model systems. The studies are performed in close
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, troubleshoot complex data issues, and critically evaluate model performance. Excellent communication abilities are required to convey findings to both technical and clinical audiences, write manuscripts, and
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on unsustainable practices and fossil fuel-based compounds as a response to societal requests for green alternatives that maintain high performance while reducing environmental impact. Surface coatings are essential
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binders, the lack of knowledge of how the modification is related to the performance is lacking. It is thus needed to collect and translate novel research for developing high-performing bio-adhesives
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battery performance. The project combines expertise in materials science, electrochemistry, and physics, and offers a collaborative environment with access to well-equipped labs and technical support. About
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, from lung organoid model systems, as well as from ethically sound free range large farm-animal models of PRLDs. The studies are performed in close collaboration with clinicians at both the Karolinska
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Optimal Control Theory Strong programming skills in C++/Python/MATLAB Familiarity with parallelization and high performance computing (CPU and GPU friendly code) Experience with Machine Learning, generative