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mouse models of immune diseases and causal learning to uncover how genes shape immune-cell states and predict how the immune system responds to interventions. This tight integration of experimental
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models of immune diseases and causal learning to uncover how genes shape immune-cell states and predict how the immune system responds to interventions. This tight integration of experimental immunology
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in vivo perturbation experiments to uncover how genes shape immune-cell states and predict how the immune system responds to interventions. This tight integration of advanced machine learning and
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perturbation experiments to uncover how genes shape immune-cell states and predict how the immune system responds to interventions. This tight integration of advanced machine learning and
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models of immune diseases and causal learning to uncover how genes shape immune-cell states and predict how the immune system responds to interventions. This tight integration of experimental
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models of immune diseases and causal learning to uncover how genes shape immune-cell states and predict how the immune system responds to interventions. This tight integration of experimental immunology
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-scale in vivo perturbation experiments to uncover how genes shape immune-cell states and predict how the immune system responds to interventions. This tight integration of advanced machine learning and
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perturbation experiments to uncover how genes shape immune-cell states and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental
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drug discovery Working knowledge on organic/medicinal chemistry Working knowledge on molecular modelling and predictive methods, ranging from empirical to machine learning approaches Experience in
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website: https://www.irc.ugent.be/groups/maelfait-lab Research Project Nucleic acids are potent triggers of immune responses. The presence of virus-derived RNA or DNA molecules activates nucleic acid