Project description

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.

Requirements for applicants

Project-specific requirements:

•     Outstanding Master's degree in Mathematics, Statistics, Meteorology or related field.
•     Demonstrated interest in the application of mathematical methods to meteorological or environmental phenomena.
•     Solid mathematical background.
•     High motivation to apply supervised learning methods.
•     Excellent command of English, both spoken and written.

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).

This a scholarship funded by the German Academic Exchange Service (DAAD) in the DAAD Graduate School Scholarship Program. 

The following DAAD requirements for applicants apply:

• Completed Master’s degree (or equivalent) at the starting date of the scholarship / preparatory German course
• Graduation no more than six years prior to nomination (exceptions apply, see DAAD website)
• No residency in Germany for more than the past 15 months before nomination ("mobility rule")
• No completed PhD degree
• Proficiency in English (German is optional)

Funding

Funding for this scholarship is provided by the German Academic Exchange Service (DAAD).

• Duration of the funding: Up to 4 years
• Monthly payments of € 1,400 (note: this will be supplemented by KIT with a part-time job with an additional income of € 603 gross per month)
• a travel allowance
• payments towards health, accident and personal liability insurance cover
• a research allowance of up to € 460
• a preparatory German language course (if available, applicable and feasible, taking into account the starting date of the scholarship)
• a material resources and supervision allowance of currently € 1,000 per year, which is paid upon application to the host institution.

Under certain circumstances, grant holders may receive the following additional benefits:

• monthly rent subsidy (calculated individually, usually about € 50 to 125 per month);
• monthly allowance for accompanying family members (about € 200 child allowance per child and about € 275 marriage allowance);
• in the case of a disability or chronic illness: subsidy for additional costs which result from the disability or chronic illness and are not covered by other funding providers