Computational Biomedicine

Jobs

For all positions, candidates should email their CV and a letter of interest to ali.farnoudnoSp@m@helmholtz-muenchen.de, including names of (ideally 3) references. The letter of interest has to be tailored to our group, mentioning projects or articles of our group that you find interesting, and explaining how you would fit on our group. Please also provide a pointer to a code repository if possible. Non-specific applications without this expression of interest or sent to a different address will not be considered.

- We have open positions for postdoctoral fellows, PhD students, or staff scientists, and welcome spontaneous applications or qualified candidates.

- In additions, we have opportunities for student assistants (HiWi), bachelor thesis, master thesis, and internships. In general, these are for a period of six months or longer, although shorter internships of 3 months are possible, in particular for local students. Besides the general information above, please include information on the lectures you have attended.

We are hiring!
We are looking for a Postdoc (f/m/x) in "Computational method development for pharmacogenomics and precision medicine". 
Deadline is the 19th of December 2021.

We are looking for a PhD student (f/m/x) in "computational method development to identify multi-omics signatures for differential diagnosis of atypical Parkinsonian syndromes".
Dealine is the 15th of December 2021.

Internships / Thesis:
We are looking for a student (f/m/x) to work on the project "Investigation of drug response in high-throughput screens using KiMONo network inference".
High-throughput screens (HTSs) data can be used to dissect signatures of cellular responses. In particular, transcriptomics is commonly used to model drug response in HTS. However, to use the whole genome can insert noise in prediction algorithms, and is expensive to investigate all the putative associations. Thus, is imperative to mine relevant genes for an effective and successful drug response in cancer cells. We propose an exploration of the multi-omics network inference tool KiMONo with several model and data parameters for a cancer type specific gene prioritization. This analysis will be complemented with a benchmark against the geno-phenotypic wppi prioritization framework. This study aims to identify cancer specific features and establish new drug targets. 

Student background/skills: Preferably, applied mathematics or computer science. R coding, graph knowledge, solid statistics, visualization tools, interest in cancer biology application

Project duration: 3-5 months, start as soon as possible.