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, with emphasis on robustness, generalization, and performance in high-dimensional and noisy biological datasets. See this publication for additional details: https://doi.org/10.1111/ede.12449 . The second
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variability. The work may include inverse problems, regularization strategies, statistical modeling, representation learning, and geometric or variational approaches to volumetric data. There is substantial
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Job description The Affinity Proteomics unit (https://www.scilifelab.se/facilities/affinity-proteomics/ ) is part of
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evolution across different genomic regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods and statistical analysis (https://cgrlab.github.io
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at Sahlgrenska Academy of relevance include genomics, metagenomics, culturomics, proteomics, transcriptomics, software development, machine learning, and other statistical analyses of large-scale health data
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to different biological materials and research questions. The bioinformatic/statistic component of the proteomic pipeline is an important part of the work, to be able to assist the users in interpretation and
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the lab and bioinformatic/statistical analyses of next-generation sequence data. The project will be conducted in collaboration with Prof. Göran Arnqvist (Evolutionary Biology Centre, Uppsala University