Publié: 01 mai 2024
Zürich
100%
Durée indéterminée
Universität Zürich
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PostDoc in Health AI
We are currently looking for a motivated PostDoc in Health AI who will work at the intersection of computation, biology and medicine. Particularly, the candidate will support our ongoing work in applying AI to the space of genetic engineering, including the prediction of base editor outcomes, the optimization of base editors and the optimal use of base editors in complex genetic diseases (see recent publications [1,2,3,4]). This work is part of the University Research Priority Program (URPP) "Human Reproduction Reloaded"
The PostDoc will be involved in other ongoing lab activities (particularly the supervision of junior lab members and some teaching activities) and will be an integral part of the URPP research community. We offer an interdisciplinary research environment, the possibility to direct your own research, access to state-of-the-art computational resources' infrastructure (further details are listed below) and a formidable place to grow academically.
References:
1- Mathis, N., Allam, A., Kissling, L. et al. Predicting prime editing efficiency and product purity by deep learning. Nat Biotechnol 41, 1151–1159 (2023). https://doi.org/10.1038/s41587-022-01613-7
2- Nicolas Mathis, Ahmed Allam, András Tálas, Elena Benvenuto, Ruben Schep, Tanav Damodharan, Zsolt Balázs, Sharan Janjuha, Lukas Schmidheini, Desirée Böck, Bas van Steensel, Michael Krauthammer, Gerald Schwank. Predicting prime editing efficiency across diverse edit types and chromatin contexts with machine learning. bioRxiv 2023.10.09.561414; doi: https://doi.org/10.1101/2023.10.09.561414
3- Mollaysa, A., Allam, A., & Krauthammer, M. (2023). Attention-based Multi-task Learning for Base Editor Outcome Prediction. ML4H Findings Track Collection, https://arxiv.org/abs/2311.07636
4- Marquart, K.F., Allam, A., Janjuha, S. et al. Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens. Nat Commun 12, 5114 (2021). https://doi.org/10.1038/s41467-021-25375-z