We have opportunities for master theses at the Institute for Computational Biology, for a period of typically at least six months. Please email a CV (including contact details of references) and a cover letter, tailored to the research group and topic (see below) you are interested in:

Research group Topic
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: Colomé-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) Deep learning and data analytics for biological and clinical image data
Data driven mathematical modeling of cell fate decisions
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) Development of computational tools for single-cell RNA-seq data analysis
Mathematical modelling of cell 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
Institute Translational Genomics (Pl: Zeggini) Causal inference and risk prediction using proteomics and whole-genome sequencing data

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