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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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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
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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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/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
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in Swedish is usually another requirement. An applicant who does not meet the requirement of proficiency in Swedish may still be hired provided they are able to actively acquire the language skills
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for precision medicine within oncology. You will develop interpretable deep learning models of tumor microenvironments from tissue images and spatial omics data. The project objective is to develop implementable
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computational biology with a focus on developing new and scalable computational models (e.g. deep learning, machine learning, optimization or statistics) or integrating data-driven applications to address
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to development projects. Establishing a research program in translational computational biology with a focus on developing new and scalable computational models (e.g. deep learning, machine learning, optimization