Open Positions

Quorum Sensing (QS) is a fascinating bacterial communication system that enables cells to sense local population density and coordinate gene expression across the community. While QS was traditionally thought to activate uniformly within clonal populations, recent studies have revealed substantial heterogeneity in QS gene expression.
Pseudomonas aeruginosa, an opportunistic human pathogen, has one of the most intricate QS networks known. It relies on three interlinked systems—Las, Rhl, and PQS—to regulate hundreds of genes involved in virulence, metabolism, and social cooperation. These systems allow P. aeruginosa to flexibly adapt its social behavior in response not just to cell density but also to spatial arrangement and microenvironmental cues.
This Master’s project aims to uncover the spatial aspects of QS dynamics and its influence on the
collective behaviors of microbial communities. You will grow P. aeruginosa as microcolonies using agarose pads and custom-designed microchips featuring artificial landscapes. High-resolution fluorescence time-lapse microscopy will be used to monitor QS gene expression in real time at the single-cell level.

To visualize gene expression patterns, you will construct a library of QS reporter strains by tagging key QS genes with fluorescent markers. The combination of time-lapse imaging and computational image analysis will allow you to study how individual cells activate QS and how this behavior spreads (or remains localized) across growing colonies.

What you will Learn

• Advanced microscopy techniques, including fluorescence and time-lapse imaging
• Hands-on work with agarose pads and microchips
• Image analysis using deep learning tools (for segmentation and cell tracking)
• Molecular biology methods (e.g., genetic engineering of reporter strains)
• Data analysis and experimental design in microbiology
• Fundamental concepts in microbial communication, cooperation, and behavioral heterogeneityOptimization of Vision Transformer (ViT) architectures to improve IMC image data analysis

What we are looking for

• Motivated Master-student with a strong interest in microbiology, molecular biology, or related
fields
• Prior experience with microscopy, image analysis, or bacterial genetics is a plus but not
required—we are happy to support you in learning new skills!
• Curiosity, enthusiasm for interdisciplinary work (combining wet lab and computational analysis)

How to Apply

If you are excited about bacterial communication, single-cell studies, and cutting-edge microscopy, we would love to hear from you! For more information, please feel free to reach out to us at xiaohe.liu@uzh.ch and rolf.kuemmerli@uzh.ch. We would be happy to discuss the project further with you!
The starting date is flexible and can be negotiated.

The Bodenmiller Lab is seeking talented and motivated Master’s students with expertise in machine learning to join our team. We offer exciting opportunities to work on cutting-edge deep learning projects focused on tumor-tissue image analysis as part of a Master’s thesis or a credit-earning assignment (e.g. unpaid internship, lab rotation, semester project).

Potential Projects

• Optimization of Vision Transformer (ViT) architectures to improve IMC image data analysis
• Enhancing explainability of pixel-based IMC models to better understand deep learning predictions
• Validating pixel-based IMC models by comparing them to single-cell analysis techniques

How to Apply

• CV (max. 2 pages)
• A short motivation letter (max. 1)
• Transcript of records (we will focus primarily on relevant coursework rather than overall grades)
• Optional: A link to a GitHub repository showcasing a relevant application
• Applications should be sent in ONE email addressed to both santiago.castrodau@uzh.ch and victor.ibanez@uzh.ch

The Kümmerli lab at the Department of Quantitative Biomedicine is looking for a master student to carry out their thesis project.

The project is titled “Does co-infection alter pathogen survival and virulence?” The project will involve working with experimentally evolved communities of the bacterial human pathogens, Pseudomonas aeruginosa (PA), Staphylococcus aureus (SA), and Klebsiella pneumoniae (KP). These opportunistic pathogens are often isolated together in both chronic and acute infections of the lung, other organs, and skin in immunocompromised patients and are a leading cause of bacteremia. The aims of the project will be to better understand the nature of interactions between the focal pathogens when they have been evolved together in an infection mimetic growth medium. This project will involve microbiology, evolutionary biology, population biology coupled with high throughput assay development, qPCR, and sequencing (if possible).

Interested applicants can write to: sukrit.suresh@uzh.ch or rolf.kuemmerli@uzh.ch for more information.

Starting dates: Early 2025.

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29.05.2024

The Swiss Institute of Bioinformatics SIB Training Group and the DQBM are hiring a bioinformatician to support training, provide researchers with bioinformatics services, and engage in activities to build a bioinformatics community in Zurich.  For further information and applying to this position please see the job ad on the SIB homepage:

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05.04.2022

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.

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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.

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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 http://radlex.org/ 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.

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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

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