In our KCDS Virtual Open House videos, you can find out about
Questions? Reach out to us at kcds∂kit.edu. We try to answer every inquiry!
Air pollution is a major problem in cities around the world. Extremely polluted outdoor air is a particular health hazard, even short-term exposure to high pollution levels causes about 1 million premature deaths annually. An early warning would help mitigate these situations but requires high-accuracy prediction. Numerical weather models are able to forecast meteorological conditions with good accuracy. However, the complex interaction between the various pollution sources and factors such as wind (for transport), precipitation (removal of pollutants), and vegetation (source and sink of particles depending on season) poses problems for the prediction of extreme air pollution with weather models.
The aim of this project is to develop a new data-driven approach to accurately predict extremes in urban air pollution from observation data, and to quantify the contributions of the various factors driving these extremes. Therefore, methods from extreme value statistics will be implemented in a machine-learning framework to estimate and predict air pollution extremes. As a starting point, we use Paris as a baseline city, where ML methods were successfully implemented to reproduce specified PM1 concentrations.This model architecture will be adapted and retrained on multi-city data from global air pollution hotspots, enabling generalization beyond the urban area of Paris.
The project lies at the intersection of innovation in mathematics and meteorology. By combining extreme value theory with machine learning, it aims to advance the current state of the art in predicting air pollution extremes.
The doctoral degree will be awarded in either Mathematics or Meteorology, depending on the candidate’s academic background.
Project-specific requirements:
General requirements:
We are looking for excellent graduates holding master degrees, received by the start of their doctoral studies at the latest, in mathematics or the SEE disciplines (natural sciences, engineering, economics), who have sufficient proficiency in mathematics and are interested in joint research that revolves around computational methods such as mathematical models, simulation methods and data science techniques.
As an international research school, we require our doctoral researchers to have good writing and oral communication skills in English (German is optional).
Project 01 is a scholarship funded by the German Academic Exchange Service (DAAD) in the DAAD Graduate School Scholarship Program.
The following DAAD requirements for applicants apply:
Funding for this scholarship is provided by the German Academic Exchange Service (DAAD).
Under certain circumstances, grant holders may receive the following additional benefits:
| Title Name | Role at KCDS |
|---|---|
| Dr. rer. nat. Andersen, Hendrik | SEE Supervisor |
| Prof. Dr. Cermak, Jan | SEE Supervisor |
| Prof. Dr. Fasen-Hartmann, Vicky | MATH Supervisor |
Cardiovascular diseases are the world’s leading cause of death, with atrial fibrillation (AF) a major contributor. AF often stems from atrial cardiomyopathy—progressive structural and electrical remodeling of the heart’s atria. Current diagnostic tools provide only static or invasive insights, limiting our ability to monitor disease evolution or tailor therapies.
This doctoral project aims to transform static “digital snapshots” of the heart into longitudinal digital twins—personalized, dynamic models that evolve with the patient. By combining data assimilation, uncertainty quantification, and computational cardiac modeling and simulation, the project will integrate invasive electro-anatomic maps with non-invasive surface ECGs to reconstruct and continuously update patient-specific atrial properties.
Within a Bayesian framework, advanced filtering methods (e.g., ensemble Kalman filters) will be developed to fuse noisy, sparse clinical data with physics-based cardiac models. This approach will enable accurate estimation of hidden physiological parameters and capture disease progression over time.
The doctoral candidate will work at the intersection of mathematical modeling, scientific computing, and cardiovascular science, jointly supervised by the Krumscheid group (uncertainty quantification and data assimilation) and the Loewe group (computational cardiology and simulation). The project offers an exciting opportunity to contribute to next-generation personalized medicine and digital health.
Project-specific requirements:
As a candidate, you should have:
General requirements:
We are looking for excellent graduates holding master degrees, received by the start of their doctoral studies at the latest, in mathematics or the SEE disciplines (natural sciences, engineering, economics), who have sufficient proficiency in mathematics and are interested in joint research that revolves around computational methods such as mathematical models, simulation methods and data science techniques.
As an international research school, we require our doctoral researchers to have good writing and oral communication skills in English (German is optional).
Project 01 is a scholarship funded by the German Academic Exchange Service (DAAD) in the DAAD Graduate School Scholarship Program.
The following DAAD requirements for applicants apply:
Funding for this scholarship is provided by the German Academic Exchange Service (DAAD).
Under certain circumstances, grant holders may receive the following additional benefits:
