Methodology: Data Science and Engineering
Overview
Data Science and Engineering develops methods for systematic analysis of large amounts of data and extracting structures, models and forecast of such data. This also includes the handling of large amounts of data that are generated with high bandwidth in distributed sensor environments. The science field is methodologically located at the interfaces between statistic data analysis, database-based data management, networked system and the methods of machine reading and artificial intelligence.
Projects
| Duration | Funding provided by | Topic |
|---|---|---|
| 2020-2022 | EFRE | Improving diagnostics for rare genetic diseases via adaptive variant Prioritization on heterogeneous clinical data (IDEA-PRIO-UR) |
| 2020-2022 | DFG | Synthesis of Petri nets based on the union/find procedure |
| 2019-2022 | ESF | Symmetrie-Erhaltung in probabilistischen Modellen (NEISS, TP 4) |
| 2014-2022 | DFG | Modeling and Simulation of Linked Lives in Demography (MoSiLLDe) |
| 2013-2022 | DFG | Efficient Simulation of Cell-Biological Multi-Level Models (ESCeMMo) |
| 2018-2021 | Industry | Data Science, Big Data, Deep Learning |
| 2018-2021 | EFRE | Sensor-based individualised activity management system for people with dementia in nursing facilites (SAMi) |
| 2017-2021 | DFG | NoSQL Schema Evolution and Big Data Migration at Scale (DARWIN) |