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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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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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may also include teaching and other departmental duties (no more than 20%). The research will focus on Numerical Analysis for Physics-Aware Deep Kernel Learning This doctoral project lies
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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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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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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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and development of algorithms, methods, and theories aimed at better understanding the properties and underlying mechanisms within statistical and deep learning-based systems also in the presence
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are robust, especially in the selected industry use cases. This staff scientist position is linked to the research group Deep Data Mining in the department of Computing Science, which focuses on fusing data
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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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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