Machine learning

We are currently looking for Master students interested to work on projects at the intersection of machine learning and medicine. Machine-Learning for the Identification of Cost-effective Clinical Pathways Do you want to combine state-of-the-art machine learning (ML) algorithms with the pressing societal problem of high (and rising) healthcare costs? Are you motivated to work with interesting real-world data and excited to implement and apply machine learning algorithms? High and rising healthcare costs are a significant challenge in many developed countries, which is expected to intensify with the demographic changes that will accelerate over the next decades.

We are currently looking for Master’s students in the field of Computer science, Statistics, or bioinformatics for the topic of Developing generative models for structured data such as molecules, proteins, genetics, and graphs. Structured data are everywhere, text, sequences, graphs, molecules, proteins, genetics, and many others. Many real-world problem can be formulated as generating structured data. For instance, generating molecules with specific properties for drug design, and designing antibodies. There has been a surge of research applying machine learning models for molecules, and protein design.

We are currently looking for Master’s students in the field of Computer science, Statistics, or bioinformatics for the topic of Developing Machine Learning Models for Drug Synergy Prediction Drug synergy is a phenomenon where the combined effect of two drugs is greater than the sum of their individual effects. While a vast amount of data exists for single drug effects on cell lines, there is a scarcity of data for drug synergy due to the huge number of possible drug combinations.