23.03.2023

The Bodenmiller lab is seeking a highly motivated postdoctoral fellow enjoying experimental methods development to work on a novel tissue imaging approach. This method relies on MALDI imaging and greatly increases the number of markers that can be measured simultaneously; it also enables multimodal tissue measurements. We have shown proof-of-concept and you will refine the method to make it broadly applicable and highly reproducible. Ultimately, you will apply it to study biomedical questions, e.g. to understand which features of a human tumor determine targeted and/or immunotherapies. You will interact with engineers, computational biologists and clinicians to make this new imaging approach reality.

More information about the position can be found here.

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06.03.2023

Digital Health Zurich (DHZ) is a new research initiative between the University of Zurich (UZH), Zurich University of Applied Sciences (ZHAW) and praxis partners (hospitals, pharma and others). The goal of the project is to investigate digital health solutions in the hospital context and implement them efficiently and with practical relevance. Core topics are Patient Reported Outcome Measure (PROMs), remote monitoring, integrated care and related technologies as well as empowerment of patients and staff. Our vision is to establish a sustainable, data-driven and cross-institutional digital ecosystem by connecting patients, health professionals, technology and research in Zurich and beyond. To accomplish this, we focus on translating research and developing sustainable real-world digital health solutions, taking the needs of patients and health providers into account, pursuing a participatory approach, supporting efficient and cross-institutional collaboration and promoting digital innovation in the Zurich healthcare system and beyond.

We are currently looking for a motivated and innovative PostDoc in Digital Health who will be responsible for clinical PROM trials (planning, execution and data analysis). The position is limited to two years with the possibility for extension. The PostDoc will be in the group of Prof. Michael Krauthammer (Chair of Medical Informatics). 

More information about the position can be found here.

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20.02.2023

We are seeking a highly motivated postdoctoral fellow who will study tumor ecosystem structure in depth and especially how this information can be used to predict patient treatments. The project will focus on suspension and imaging mass cytometry experiments using human tissues and organoid models in order to understand tumor ecosystem rules that can guide treatment selection for patients. The project will be associated with two clinical projects (Tumor Profiler and Swiss Precision Oncology) providing a unique opportunity to collaborate with clinicians, statisticians, and computational scientists to bring real benefit to cancer patients. The postdoctoral fellow will be located in Zurich.

More information about the position can be found here.

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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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