PostDoc in Health AI

The University of Zurich together with the University Hospital of Zurich are embarking on a concerted effort to develop informatics programs to advance biomedical research and healthcare using cutting edge computational approaches. As part of these efforts, the Chair of Medical Informatics (krauthammerlab) investigates topics in clinical data science and translational bioinformatics, such as knowledge discovery from Big Data sources (Electronic Medical Records, health registries) as well as the analysis of human Omics data.

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.

• PhD degree in computer science (focused on machine learning), optimization, statistics, applied math, computational biology or closely related discipline.
• Strong publication record with at least one paper in top-tier conferences/journals (such as NeurIPS, ICML, ACL, CVPR, AISTATS, AAAI, ICLR, KDD, IJCAI, ML4H, Nature Communications, Nature Biotechnology, etc.)
• Proficient in Python and the scientific computing stack (SciPy, Numpy, Scikit- learn, pandas)
• Proficient in one of the deep learning frameworks (PyTorch, Tensorflow)
• Prior health- and biology- related computational work is a plus.

• Access to state-of-the-art infrastructure (computational resources), clinical datasets and medical expertise domain-knowledge (excellent medical doctors and research scientists)
• Ability to make a real and tangible impact in healthcare research
• Solve real-world problems and improve hospital-related processes and workflow
• Stimulating research environment and a place to grow academically and professionally
• Outstanding working conditions at the University of Zurich

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

Ahmed Allam