We are looking for Masters Students!

We are currently looking for Master’s students in the field of Bioinformatics for the topic of

Nucleosome footprints in cell-free DNA sequencing data

Cell-free DNA (cfDNA) is released by dying cells into the surrounding tissues and also to the bloodstream. As nucleosomes protect DNA from degradation, plasma cfDNA carries information about the nucleosome organization in the cells of origin. Different characteristics of cfDNA are increasingly being used in the diagnostics of genetic diseases and the monitoring of cancer. We discern nucleosome footprints at transcription binding sites and DNase hypersensitivity sites from cfDNA-sequencing data and infer the contribution of different cell / tissue types to cfDNA. In doing so, we aim to quantify cancer-derived cfDNA and detect inflammation in various tissue types.

We are looking for students who have some experience with Unix systems, proficiency in 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:

• Feature extraction from cfDNA-sequencing data or
• Transfer learning and batch effect correction across cfDNA-sequencing datasets or
• Representation learning from high-dimensional cfDNA-sequencing data

You can apply by sending a CV to this e-mail along with a short description of your motivation to join our lab.

For CBB master students at ETH, please check this form .

For other students at ETH - you need be enrolled at UZH as a mobility student, see here . If you have any questions regarding ETH regulation, please contact Student Exchange Office: Dr. Francesca Broggi, francesca.broggi@akd.ethz.ch, Tel +41 44 632 43 46.

References

1 Snyder, M. W. et al. Cell 164, 57–68 (2016)

2 Peneder, P et al. Nat Comms 12, 3230 (2021)

3 Rao, S. et al. Sci Adv 8, 34 (2022)

We are currently looking for Master’s students in Clinical Data Science for the following topics

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.

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.