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Department of Quantitative Biomedicine

Open Positions

NLP Engineer in the Krauthammer lab


The Krauthammer lab is looking for a Machine Learning NLP Engineer with a passion for working on medical problems that can help us create NLP tools in the medical field. You will be someone who loves to code and build working systems. You are used to working in a research environment. You will have experience with the software development life cycle, from ideation through implementation to testing and release. You will also have extensive knowledge and experience in the NLP domain.

You will join a group of 3-4 NLP researchers in the Krauthammer Lab and will have the opportunity to collaborate with multiple research teams at the Hospital University of Zurich and the University of Zurich. In this position, you'll be working at the heart of our NLP Team, helping us work on the automatic generation of medical reports from medical images and as well as helping to convert unstructured medical text to structured information. Topic-wise, you will work 50% on radiology data (collaboration Department of Radiology USZ), and 50% on more general medical data contributing to a new medical research ecosystem called Biomedical Informatics Platform (BMIP, Collaboration the LOOP Zurich). You will also help us create high-quality, production-ready code and take ownership of production pipelines.

More information about the position can be found here.


PhD Students in the Joller lab


PhD students interested in joining the Joller lab are exclusively recruited through the Microbiology and Immunology program of the Life Science Zurich Graduate School.

Application deadlines are July 1st and December 1st every year.


Master Students and Medical Students in the Joller lab

Flexible starting date

We regularly have openings for motivated UZH and ETH students with a strong interest in immunology. To discuss possible projects, please send your CV and a few sentences on why you would like to join our lab to Nicole Joller.


Computational Master Student Projects in the Krauthammer lab

Flexible starting date

The Krauthammer lab is currently looking for Master’s students in the field of Bioinformatics for the following topics:

1. Analyzing cell-free DNA fragmentation patterns in clinical samples

This project aims to improve diagnosis and prognosis through liquid biopsies in cancer and various other diseases. Cell-free DNA (cfDNA) carries information on the epigenetics of distal tissues and organs. We discern epigenetic signatures from cfDNA-sequencing data and connect those to diagnoses and patient outcomes. The student should have basic experience with Unix systems, some experience with at least one scripting language (e.g. Python or R), a strong understanding of biostatistics and a basic understanding of machine learning algorithms. Depending on the experience and interest of the student we offer projects focusing on either:

  • Feature extraction from cfDNA-sequencing data using bioinformatic tools and comparison to nucleosome coverage or methylation data or
  • Representation learning from high-dimensional cfDNA-sequencing data

2. Data integration in Cancer Genomics

The project’s aim is to characterize the effects of transcriptional and epigenetic processes on mutational patterns in cancer. The student should have basic experience with Unix systems and some experience with at least one scripting language (e.g. Python or R). Prior experience with genomics software and data formats is an advantage.

The Krauthammer lab is currently looking for Master’s students in Clinical Data Science for the following topics:

3. Information extraction from German Radiology Report

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. There are some English radiology report datasets that have been labeled automatically to detect in presence of 14 observations by capturing uncertainties inherent in radiograph interpretation. We would like to investigate different approaches such as the information extraction paradigm to label radiology reports in another language such as German. We plan to explore the possibility of incorporating domain knowledge such as and evaluate the effectiveness of it in the proposed framework. Since a radiology report comes with radiology images, we also would like to investigate the multi-modal approaches in our study.

4. Trait prediction using Big data and machine learning

Genome-wide association studies rely on big cohorts of hundreds of thousands of participants with gene sequences amounting to TB of information. This project is interested in predicting relevant traits like disease risk and identifying this risk’s genetic component. We also are interested in resolving possible confounders in this kind of data, like pairwise interactions.

The model and data reading are already implemented, objectives of a short project would be to:

  • Accelerate data reading and training
  • Perform experiments on performance and hyperparameter tuning.
  • Select the best performing predictor and test in an external cohort.

The student should have experience with Unix Systems, good experience with Python and ML frameworks (preferably TF 2). Additional experience with quantitative genetic tools like plink would be advantageous.

Applications can be done by sending a CV to this e-mail along with a short description of the student’s motivation to join our lab.


Master Student positions in the Bodenmiller lab

Flexible starting date

The Bodenmiller lab is seeking to fill several Master Student positions, with flexible starting date. These projects are in the field of

  • Quantitative biology I: Simultaneous analysis of up to 100 markers in tumors using a new technology, called imaging mass cytometry [Link]
  • Quantitative biology II: Analysis of how tumor cells become metastatic
  • Computational biology: Computational analysis of signaling networks

Please send your CV and a few sentences why you would like to join our lab to Bernd Bodenmiller



Weiterführende Informationen

Life Science Zurich Graduate School

Life Science Zurich Graduate School

Next LSZGS application deadline: 1 July 2023

BSc-, MSc & PhD Theses

BSc-, MSc- and PhD students are welcome to write their thesis at the DQBM. Please contact the group leader of your choice to ask about opportunities.