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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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Department of Biochemistry and Biophysics . Main responsibilities We are looking for 1-2 bioinformatics experts that will participate in analysis of spatial omics data and/or advanced biostatistics/machine
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing
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or explainable AI or safety). Experience in machine learning, causal inference, image processing, human-robot interaction, or large language models. Experience in analyzing multimodal data (e.g., text, sensor
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managing large amounts of data by designing structured databases (PostgreSQL, MySQL). Machine learning methods such deep learning for analysis of proteomics data and classification of cancer profiles. Since
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing
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with machine learning and generative AI algorithms, with working knowledge of deep learning frameworks such as PyTorch or TensorFlow is considered a strong advantage. • Extensive experience in multi
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advanced biostatistics/machine learning analyses, but also with other types of analysis. The work involves supporting Swedish researchers under a “user fee-based” support model. The projects will differ in
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interdisciplinary, applied research with expertise in visualization, design, computer graphics, and the learning sciences. The research nexus for the division is the Visualization Center C, a unique science center in
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This PhD project focuses on strengthening network security for large-scale distributed AI training. As training increasingly spans multiple data centers connected over wide-area networks, it