We are looking for Masters Students!

We are 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.

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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Michael Krauthammer
PI

Interested in (a) moving data-driven solutions into patient care and (b) knowledge discovery from big biomedical data sources

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