Helmholtz’ data science Projects

 

1. Machine learning based attribution of flood risk trends 

2. Quantum machine learning for remote sensing

3. Validation of earth observation data product

4. Curvature based segmentation in microscopic images of the human brain

5. Multi-source data fusion for rapid flood mapping

6. Optimization of data distribution

7. automatic Meta Data Generation

8. Software vulnerability detection using deep learning

9. Repository Mining of Models

10. Tell-me-what-they-liked

11. Quality Prediction for Welding

12. Large Scale Transfer Learning applied to X-Ray based COVID-19 diagnostics

13. Multiple Instance Learning-based MHC-I Epitope Prediction

14. Magma production of terrestrial planets

15. Predicting cognitive performance from brain images

16. Image processing & segmentation for machine learning evaluation

17. Explainable AI (XAI) Approach to Support Explainability of Complex Classifiers

18. Using data science to extract submesoscale features from high-resolution aerial remote sensing data

19. Ontology Characterization

20. Automated detection of multi-class membrane proteins in cryo-ET

21. Predicting temperature dependent solar-module performance

22. Machine learning for railway vehicle and track monitoring

23. Development of a Surrogate Model for Matter under Extreme Conditions

24. Brain structure-function association and complexity

25. Neural network approach to the reverse transformation of spin-wave theory

26. Data preparation und analysis of greenhouse telemetry data

27. Adhoc MAP uncertainties for normalizing flows

28. Requirements for a training set to achieve the desired level of performance in detection of anomalous railway switch condition

29. Statistical compositional data science for microbiome data

30. Deep learning and mathematical modelling in hematopathology

31. ML for the identification of Lagrangian flow features

32. Open-source R Package for modelling data with continuous piecewise linear functions

33. Wait, What? Discovering Surprising Commonsense Aspects of a Knowledge Graph