Opportunities

Patient*innenperspektiven zur Akzeptanz und Nutzbarkeit digitaler Gesundheitstechnologien in der Onkologie - eine qualitative Studie

Digitale Werkzeuge wie Conversational Agents und Wearables bieten vielversprechende Möglichkeiten zur Erfassung von Patient-Reported Outcomes (PROs) und objektiven Krankheitsverläufen, wodurch das Wohlbefinden der Patientinnen verbessert werden kann und eine personalisierte Behandlung in der Onkologie ermöglicht wird. Die erfolgreiche Einführung und Umsetzung dieser Werkzeuge in herkömmlichen Gesundheitssystemen kann jedoch Herausforderungen mit sich bringen. Schlüsselfaktoren sind die Einbindung der Patientinnen, die Akzeptanz und die Anwendungsfreundlichkeit des Systems. Um diese entscheidenden Aspekte anzugehen, ist ein nutzungsorientrierter Ansatz unerlässlich, der sich darauf konzentriert, die Bedürfnisse der Patient*innen zu verstehen und technologiebedingte Barrieren zu überwinden.

Software development engineer 80 - 100 %

The LOOP Zurich is a medical center for translational research and precision medicine. It focuses on new approaches, better diagnostics, and novel therapies that will benefit patients and society as a whole. The goal is to enhance personalized healthcare by efficiently transferring scientific knowledge into medical practice. As part of its strategy, The LOOP Zurich is developing an IT research platform (“LOOP BMIP Data Platform”) between the major Zurich hospitals and academic partners (University of Zurich and ETHZ) of strategic importance to the Zurich medical research ecosystem.

We are looking for Masters Students!

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 looking for Masters Students!

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 looking for Masters Students!

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.