Open Positions

Ph.D. Position in A.I. for healthcare 100%

04.10.2021

The Krauthammer lab is looking for a motivated PhD candidate to work on projects in the intersection of healthcare, data science and machine learning with special emphasis on the analysis of patient's longitudinal data.
 
Our goal is to develop state-of-the-art approaches and build best-in-class methods to capitalize on digital clinical information to automatically compare, analyze and visualize complex longitudinal patient journeys focusing on the concept of patient journey similarity. This involves building robust decision support systems powered by (explainable and interpretable) predictive algorithms for guiding patient therapy across all disease stages, the assessment of treatment effects using counterfactual inference and the identification of causal mechanisms driving disease progression. Moreover, there are possibilities to expand the research scope towards working with heterogeneous biomedical data for patient stratification.
 
We offer an interdisciplinary research environment, the possibility to direct your own research and access to state-of-the-art computational resources infrastructure.


For more information, please see the full ad here
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Senior computational staff scientist 100%

08.01.2021

The Bodenmiller lab is recruiting a senior computational biologist or bioinformatician to support the analysis of high-dimensional and spatially resolved single-cell data. 

For more information, please see the full ad here
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Genomic Data Scientist 100%

20.11.2020
The Polymenidou lab is looking for a genomic data scientist to work closely with our experimental team to analyse and explore a large collection of high-throughput sequencing data. Experience in RNA-seq data analysis, including bulk transcriptomics, splicing analysis and single cell analysis is required. The ideal candidate will also have expertise in analyzing high-throughput sequencing datasets to determine the function of RNA-binding proteins, including CLIP-sequencing and related methodologies.

The successful candidate will be fully integrated in both the Polymenidou lab and the Robinson teams. The candidate will, therefore, combine work in neurodegeneration and RNA biology (Polymenidou) with statistical methods and data science tools (Robinson). 

For more information, please see the full ad here
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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:

  • 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.

    Mapping structural variation in cancer
    Genome sequencing data are going to be used in order to discover novel structural variants in various cancer types (melanoma, colorectal carcinoma, breast cancer, etc.). The clinical significance of the novel and already known structural variants will also be evaluated using genome annotations. The student should have basic experience with Unix systems and some experience with at least one scripting language (e.g. Python). Prior experience with genomics software (aligners, samtools or variant callers) is an advantage.

    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. The student should have basic experience with Unix systems, some experience with at least one scripting language (e.g. Python), a strong understanding of biostatistics and a basic understanding of machine learning algorithms.

    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.

    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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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

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