Our collaborators at the USZ Institute of Intensive Care Medicine, specialising in recovery after neurosurgery are looking for Master students in ML for respiratory failure prediction and prevention.
Check out the call on their website For questions and for applying you can turn to Una Pale.
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 in the field of Bioinformatics for the topic of
Nucleosome footprints in cell-free DNA sequencing data Cell-free DNA (cfDNA) is released by dying cells into the surrounding tissues and also to the bloodstream. As nucleosomes protect DNA from degradation, plasma cfDNA carries information about the nucleosome organization in the cells of origin. Different characteristics of cfDNA are increasingly being used in the diagnostics of genetic diseases and the monitoring of cancer.
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.