We developed machine learning models, PRIDICT2.0 and ePRIDICT, to predict prime editing efficiency, offering a robust tool for optimizing genome editing strategies across diverse chromatin contexts.
PRIDICT is a machine learning model that accurately predicts prime editing efficiency, validated across diverse genetic edits and various experimental conditions.
Viral testing for pediatric inpatients with respiratory symptoms is common, with considerable associated charges. In an attempt to reduce testing volumes, we studied whether data available at the time of admission could aid in identifying children …
The Wicki Lab (at the University and University Hospital of Zurich) and the Krauthammer lab (at the University of Zurich) are seeking a motivated PhD candidate to join a recently established team from both labs working on the AI Tumorboard project improving precision oncology. The candidate will develop and apply novel AI models for the analysis of multi-omic data to advance cancer treatment decisions.
Your Responsibilities The goal of this position is to combine genomics and machine-learning approaches to improve personalised cancer care.
The Krauthammer lab (at the University of Zurich) and the Wicki Lab (at the University Hospital of Zurich) are seeking a motivated PhD candidate to work on AI-driven research at the intersection of precision oncology and data science, with a particular focus on leveraging multi-modal clinical data to support personalized cancer treatment decisions.
Your Responsibilities The primary goal of this position is to develop state-of-the-art machine learning approaches to enhance personalised decision-making in oncology care in the following aspects:
We are currently looking for Master’s students with a background in machine learning (or related computational field) for a project on Protein Fitness Optimization.
Protein fitness optimization aims to improve a protein’s functionality by modifying its amino acid sequence to enhance a specific property, such as stability or binding affinity. There are various computational approaches to tackle this problem that enable in-silico pipelines to suggest new protein candidates. One of them involves generative models, which aim to capture the distribution of protein sequence data and propose new sequence mutants based on the learned distribution.