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research involving sampling and detailed characterization of lime and cement clinker products from electrified manufacturing processes. The position is full-time for two years, starting on November 1, 2025
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for multimodal machine learning, combining large-scale image data with molecular profiling and clinical data. This includes, for instance, research on deep learning-based image analysis and data assimilation
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sampling and detailed characterization of lime and cement clinker products from electrified manufacturing processes. The position is full-time for two years, starting on November 1, 2025 or as agreed
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to Reionization from observational and numerical perspectives. Main responsibilities The observational projects will involve analysis of both imaging and spectroscopic data obtained from JWST, HST, and other
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machine learning approaches Implement and manage workflows for large-scale data processing on high-performance computing systems Design and perform validation experiments through tissue imaging, molecular
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are: DWI-MRE: Adapt current MRE sequences to estimate both MRE and diffusion-weighted images (DWI) at once. MD-MRE: Adapt DWI-MRE to use free gradient waveforms as done in MD-dMRI. Process MD-MRE using
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a focus on visual language grounding, in other words, the linking of elements of natural language (words, phrases, or sentences) to visual inputs (such as images or video) in a meaningful way. The
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artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National
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and cost-effective solutions integrating renewable energy sources, electrification of heat, and energy storage are deemed. We aim to design and prototype different technologies, including low and high
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grounding, in other words, the linking of elements of natural language (words, phrases, or sentences) to visual inputs (such as images or video) in a meaningful way. The position is part of an on-going