-
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
-
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
-
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
-
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
-
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
-
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
-
evolutionary analysis. A central component of the research will be to develop machine learning and deep learning methods trained on coding sequences and protein structure to extract patterns in data and to draw
-
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
-
/deep learning to improve workflows related to antibody engineering. Have documented experience from development of therapies for oncology applications. Have or have had a postdoctoral appointment. Have