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spatial mass spectrometry. Experience with single-cell omics is also an advantage. Advanced biostatistics and machine learning, such as multivariate analysis, regularization, deep learning, or network
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and accepted to the PhD program at Stockholm University. Project description Project title: “Deep learning modeling of spatial biology data for expression profile-based drug repurposing”. A new exciting
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the molecular level. While structural predictions using deep learning methods like AlphaFold have revolutionized our understanding of sequence dependent molecular structure, we currently have much more limited
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spatial proteomics, spatial mass-spec. Experience with single-cell omics is also an advantage. advanced biostatistics and machine learning, such as multivariate analysis, regularization, deep learning
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implementing AI/ML methods (e.g., machine learning, deep learning) for life science research. Collaborating with research groups to identify needs and opportunities for AI/ML support in their projects
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will find yourself in a team that values creativity and allows you to influence the decisions made within the group. Furthermore, we value continuous learning and encourage you to allocate time for
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for statistical computing and data visualization Deep learning frameworks, such as PyTorch or Tensorflow and data science tools such as Numpy, Pandas and Matplotlib Experience in machine learning management systems
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SciLifeLab. To be successful in this position you need a deep understanding of the emerging research field virtual cells, at the interface of advanced molecular cell biology and imaging on the one hand and
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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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spectrum, in topics in virology and immunology, and currently specializes in computational biology focusing on developing methods and applications of deep learning for protein sequence and structure, as