PhD student / PostDoc (f/m/x) in Single-Cell Systems Biology
The University of Bonn is an internationally operating research university with a broad spectrum of subjects. With 200 years of history, around 38,000 students, more than 6,000 employees and an excellent reputation: The University of Bonn is one of the most important universities in Germany. In the recent excellence initiative, the University of Bonn was the most successful university in Germany with six Clusters of Excellence.
In this excellent scientific environment, the Interdisciplinary Research Unit Mathematics and Life Sciences (https://www.mathematics-and-life-sciences.uni-bonn.de/en) develops and applies novel mathematical approaches and software tools for data analysis and modelling. Application areas include cancer and immunology, with an emphasis on high-throughput (single-cell) data. We are intensively collaborating with world leading experts in mathematics and immunology in the Hausdorff Center for Mathematics, ImmunoSensation2, and the German Center for Neurodegenerative Diseases. Currently, we are searching for individuals who strengthen our team.
Project description: Single-cell RNA sequencing has in recent years revolutionized the understanding of biological processes. The available computational data analysis methods focus however mostly on static snapshots and do not provide information about population dynamics, e.g., treatment responses or during disease progression. In addition, although it is well established that cell-cell communication plays a crucial role in many diseases, our understanding is still very limited. To interpret time-resolved datasets, novel modelling and analysis approaches are required.
The goal of this project is to develop mechanistic models or machine learning approaches to integrate single-cell data for the identification of biomarkers and the unraveling of crosstalk mechanisms. The work will be based on established approaches as well as on our recent advances in mechanistic modeling for single-cell RNA sequencing data (Fischer et al., Nature Biotechnology, 2019). The approaches will be applied in collaborative projects with the partners at the University of Bonn and the German Research Center for Neurodegenerative Diseases, in particular, the Schultze lab and the Schlitzer lab. Applications will focus on chronic obstructive pulmonary disease (COPD) and Alzheimer.
- Development of mathematical model for single-cell data
- Development of methods for the model-based analysis of single-cell data
- Implementation and evaluations of models and methods
- Application of methods and interpretation of the results
- Collaboration with national and international partners
- Master / PhD degree in bioinformatics, mathematics, physics, or equivalent
- Background in statistics and machine learning
- Programming experience (preferably R, Python and/or C++)
- Experience with large-scale datasets is an asset
- Being comfortable in interacting with colleagues in an interdisciplinary setting
- Proficiency in written and spoken English
- Passion for science and scientific work
- Working in an innovative, well-equipped and scientifically stimulating environment
- Further training opportunities
- Initial short-term employment contract for 2-3 years with a standard public service salary (TV-L EG 13, Phd: 75% / PostDoc: 100%)
The University of Bonn is committed to diversity and equal opportunity. It is certified as a family friendly university. It aims to increase the proportion of women in areas where women are under-represented and to promote their careers in particular. It therefore urges women with relevant qualifications to apply. Applications will be handled in accordance with the Landesgleichstellungsgesetz (State Equality Act). Applications from suitable individuals with a certified serious disability and those of equal status are particularly welcome.
The deadline for the application round is October 31, 2019. Application documents (cover letter, CV, certificates, and two reference letters or contact details of former advisors) should be submitted as soon as possible as a single PDF file via email.
Contact: Prof. Dr. Jan Hasenauer, firstname.lastname@example.org