Student Jobs

We have opportunities for master thesesstudent assistants (HiWi jobs) and internships, for a period of typically at least six months. These positions can be funded (financially supported) by ICB, as long as they are not mandatory university course work.

Candidates should email their CV (including contact details of references) and a cover letter of interest to anna.sachernoSp@m@helmholtz-muenchen.de. The cover letter has to be tailored to each research group individually (see group pages) and explaining how your research background would fit. In your cover letter, please refer to recent publications you are interested and why.  Non-specific applications without this tailored expression of interest or sent to a different address will be not considered.

Optimization of Patient Treatment (PI: Ahmidi) Investigating patients conditions and quality of their treatments in hospital
Developing visualization tools for patients treatments in hospitals
Scientific Computing Research Unit (Pl: Castell)
Computational Epigenomics (Pl: Tatché)
Computational Biology (Pl: Filipp) Precision medicine of malignant melanoma
Epigenomic regulatory networks in cancer
Cancer Metabolism
Genetic and Epigenetic Gene Regulation (PI:Heinig) Causal inference using polygenic risk scores and gene expression
Translational Bioinformatics (Pl: Marsico) Machine learning methods for post-transcriptional regulation of gene expression
Analysis of microRNAs in embyonic development
Quantitative Single Cell Dynamics (PI: Marr) Biomedical image computing
Data driven mathematical modeling
Computational Biomedicine (PI: Menden) Machine learning / biostatistics methods for pharmacogenomics (drug high-throughput screens)
Systems biology analysis of cancer and diabetes
Translational Immunoinformatics (PI: Schubert) Machine learning and Combinatorial Optimization in Computational Immunology
Biostatistics analysis and methods development for Biomedical applications
Physics and data-based modeling of cellular decision making (PI: Scialdone) Developing computational tools for single-cell RNA-seq data analysis
Mathematical modelling of cellular fate decision
Machine Learning (PI: Theis) Development and application of machine learning techniques for the analysis of scRNA-seq data/ Artificial intelligence in biomedical data science
Artificial intelligence in biomedical data science
Institute Translational Genomics (Pl: Zeggini) Causal inference and risk prediction using proteomics and whole-genome sequencing data

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