The Data Integration in the Life Sciences (DILiS) research group at the Institute of Computer Science at Freie Universität Berlin is headed by Katharina Baum. We primarily investigate and develop methods for data analysis and integration. Our research questions often originate from the life sciences or the (public) health sector. We integrate different levels of data and develop combined computational methods using network-based analysis, machine learning and dynamic modeling. We are a young, dynamic team at the idyllic Dahlem campus and teach mainly in the Computer Science, Bioinformatics and Data Science programs.

Job description:
The advertised position is planned for work in our EU project Waste Water Pathogens (WaWaPa). The project of the European Health and Digital Executive Agency (HaDEA) is a collaboration under the leadership of the Instituto de Desenvolvimento de Novas Tecnologias (UNINOVA) in Lisbon and includes collaborations within the FUB and internationally. As part of the project, water samples are regularly taken from various locations and subsequently analyzed metagenomically and tested for known pathogens. The advertised position will deal with the presentation, visualization and analysis of the resulting multivariate time series data for different groups of stakeholders. In addition, further accompanying data levels (e.g., mobility, weather, etc.) are to be collected and integrated as part of the project. Another important task area is the development of further integrative methods for data analysis, for example using networks, mathematical modeling and machine learning.

Requirements:
Master’s degree in a relevant field, e.g., in Computer Science, Data Engineering, Data Science, Mathematics, Bioinformatics, or other related fields

Desirable:

- Demonstrated ability to develop well-designed and performant front-end applications (like GUIs, Dashboards) and experience with or strong interest in user-oriented data visualization techniques
- Experience on interfacing front-end with a database management system, e.g., PostgreSQL, working with data models
- Experience in data preprocessing, working with noisy and diverse data
Demonstrated ability to deliver high-quality documentation of data and its preprocessing, code and workflows
- Excellent communication abilities in written and spoken English for communication within the team and with collaboration partners, knowledge of German is beneficial
- A solid theoretical background in statistical data analysis, time series analysis, mathematical modelling, networks and their analysis, techniques of machine learning
- Experience working with data from a microbiological context and/or with additional data types relevant to wastewater or infectious diseases is beneficial
- Thorough, reliable and team-oriented working style
- Gender- and diversity awareness

For further information, please contact Prof. Dr. Katharina Baum (katharina.baum@fu-berlin.de ).