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samples, lack of training data and sample variability. In this project we aim to develop AI/ML workflows for improved quantitative analysis of LNPs. Your responsibilities will include optimisation of data
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processing of personal data in the recruitment process. It may be the case that a position at KTH is classified as a security-sensitive role in accordance with the Protective Security Act (2018:585
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), statistical analysis of LHC data or beyond-the-Standard-Model phenomenology, is meriting. Experience with large-scale training on GPU and HPC systems, with design of experiments and active learning, with open
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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Systems and Software Program (WASP ). You can find more information about us on the Department of Information Technology website. The position is hosted by the Division of Scientific Computing (TDB), one
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processing of personal data in the recruitment process. It may be the case that a position at KTH is classified as a security-sensitive role in accordance with the Protective Security Act (2018:585
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existing genomic datasets and enable analysis of gene regulation at cell-type resolution. The project places particular emphasis on ensuring high data quality and developing robust methods that can be
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an outstanding and ambitious postdoctoral researcher in computational biology to pioneer understanding and modeling of tissue architecture using single-cell and spatial transcriptomics data. The focus will be
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funding has been secured through a prestigious grant from SciLifeLab, a Swedish national center for advanced research and one of Europe’s leading molecular biology laboratories, driving innovation in
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of molecular networks in cancer to advance precision medicine. By integrating high-throughput data (e.g. transcriptomics, proteomics, metabolomics) with prior knowledge of molecular interactions, we construct