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Linking adaptive immune repertoire sequencing and artificial intelligence
Immune repertoires represent a vast and diverse collection of T and B cell receptors which are able to interact with a seemingly infinite number of foreign pathogens. Recent advances in deep sequencing, microfluidics and machine learning allow us to relate and predict the relationships between immune receptor sequence, gene expression, and functional properties at the single-cell resolution. Using such approaches, we can both quantify clonal selection of immune populations and also generate novel immune receptors with specific biophysical properties.
Projects involve investigating how machine learning/artificial intelligence can be integrated with adaptive immune repertoire sequencing. Both computational and experimental projects are available at ETH Zurich (Basel, Switzerland). Computational projects are available at UMC Utrecht (Utrecht, Netherlands). Computational projects may be performed remotely pending on the exact circumstances.
Projects involve investigating how machine learning/artificial intelligence can be integrated with adaptive immune repertoire sequencing. Both computational and experimental projects are available at ETH Zurich (Basel, Switzerland). Computational projects are available at UMC Utrecht (Utrecht, Netherlands). Computational projects may be performed remotely pending on the exact circumstances.
Not specified
For more information, please contact Alex Yermanos directly (A.D.Yermanos@umcutrecht.nl).
For more information, please contact Alex Yermanos directly (A.D.Yermanos@umcutrecht.nl).