PhD position - "Smart preparation of air pollutant satellite retrievals for optimizing emission inventory data" within the HDS-LEE graduate school

Human activities—particularly increasing energy production and consumption, but also natural processes—release large quantities of trace substances, thereby influencing the composition of the atmosphere and the climate. Since the beginning of industrialization in the 19th century, the chemical composition of the air has changed significantly and today contains substantially higher levels of trace substances. This has two important consequences for the Earth system and human society: On the one hand, trace substances influence the Earth’s radiation balance and climate; on the other hand, higher levels of pollution—particularly in the world’s industrialized countries and megacities—lead to health impacts, damage to ecosystems, and crop losses.

The Institute of Climate and Energy Systems - Troposphere (ICE-3) investigates the chemistry and transport of trace substances in the troposphere. To this end, field and simulation chamber experiments as well as model simulations are conducted. Our research in the “Atmospheric Modeling” division at ICE-3 aims to model and predict the composition of the atmosphere. The research focuses on multiphase processes, the role of tropospheric oxidants, and the role of pollutants in the gaseous and condensed phases on air quality. It also investigates the extent to which observations are predictable and whether known emissions of air pollutants can explain the observed pollutant concentrations.

Your Job

To make reliable predictions and analyses of air pollution, data assimilation methods, which integrate observational data into model simulation to update the model state and the underlying parameters, are inevitable. Specifically, the improvement of emission inventory data by data assimilation applications has demonstrated strong positive impact on the analysis’ performance. However, recent studies have demonstrated the limitations of current ground- and satellite-based observational networks.

The PhD project aims for a new method to derive ground-level concentrations from satellite-based measurements and additional data. Specifically, the project’s objective is to develop a data-driven method that maps satellite-derived data to ground-level concentrations for air pollutants such as NO2 (nitrogen dioxide), CO (carbon monoxide), and SO2 (sulfur dioxide). The methodology will be embedded into the data assimilation workflow of the European Air pollution Dispersion-Inverse Model (EURAD-IM). Focus will be placed on the Sentinel-5P TROPOMI (Tropospheric Monitoring Instrument) data, which is available since 2018 and thus provides a big dataset. However, the transferability to Sentinel 4 and 5 data recently launched in July and August 2025 will be explored. All this has the potential to enhance the performance of emission data analyses to the next level.

The outcome of the project is twofold. Firstly, the project aims at developing a method that can derive ground-level concentrations of air pollutants on a high spatial resolution directly from satellite retrievals. Secondly, the outcome of this research will enable the sampling of satellite data to identify an optimal subset of observational data for optimizing the emission inventory data. Analyses of these emission corrections will be performed with the four-dimensional variational data assimilation (4D-var) system within the chemistry-transport-model EURAD-IM. The simulation improvements due to optimized emission data will be evaluated with independent observations, such as vertical profile data from the In-service Aircraft for a Global Observing System (IAGOS).

The project will be conducted as a collaboration of the Air Quality and Emission Optimization group at the Institute of Climate and Energy systems (ICE-3) at Forschungszentrum Jülich and the group working on Methods for Model-based Development at RWTH Aachen. The PhD candidate will be based at Forschungszentrum Jülich where the scientific focus on atmospheric research is placed. To exchange and deepen the collaboration in particular with respect to methodological approaches of data-driven solutions, at least two short but intensive research stays at RWTH Aachen are planned. It is envisioned that this strengthens the communication and advances the progress of research.

Your tasks involve:

Developing a machine learning-based model to map satellite retrievals to ground based air pollutant concentrations
Conducting error assessment on the derived concentration data
Implementing new observational data into an operational data assimilation system
Evaluating potentials and limitations of the integration of the derived data for air quality modelling and analyses
Presenting research results at national and international conferences
Preparing and contributing to scientific publications

Further information onb the project is available at: https://www.hds-lee.de/admission/openpositions/

Your Profile

Mandatory qualifications are:

M. Sc. degree in meteorology, physics, mathematics, or a related field
Good knowledge in data handling and machine learning
Good knowledge in software development, data processing and visualization with Python
Strong interest in atmospheric physics, chemistry, atmospheric modelling, and observations
Outstanding organizational skills and the ability to work independently
Very good cooperation and communication skills and ability to work as part of a team in an international and interdisciplinary environment

Of great advantage are:

Experiences in numerical modelling
Basic knowledge of data assimilation
Experiences on high performance computing (HPC)
Knowledge of FORTRAN programming language

Please feel free to apply for the position even if you do not have all the required skills and knowledge. We may be able to teach you missing skills during your induction.

Our Benefits for You

We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! This HDS-LEE PhD position will be located at Forschungszentrum Jülich and RWTH Aachen. We offer ideal conditions for you to complete your doctoral degree:

Outstanding scientific and technical infrastructure
A highly motivated group as well as an international and interdisciplinary working environment at one of Europe’s largest research centres
Continuous scientific mentoring by your scientific advisors
Chance of participating in international conferences
Unique HDS-LEE graduate school program (including data science courses, soft skill courses and annual retreats) https://www.hds-lee.de/about/
Qualification that is highly welcome in industry
Further development of your personal strengths, e.g. via a comprehensive further training program; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: https://www.fz-juelich.de/judocs
30 Days of annual leave and flexible working arrangements, including partial remote work
Targeted services for international employees, e.g. through our International Advisory Service
Access and work on one of the world's leading HPC infrastructures at JSC

The position is initially limited to three years, with a planned one-year extension. Pay in line with 75% of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund) and additionally 60 % of a monthly salary as special payment („Christmas bonus“). Further information on doctoral degrees at Forschungszentrum Jülich including our other locations is available at: -juelich.de/gp/Careers_Docs

In addition to exciting tasks and a collegial working environment, we offer you much more:

We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.

The following links provide further information on diversity and equal opportunities: and on specific support options for women:

Place of Employment: Jülich

**Start Date:**To the next possible date

Salary: Pay group 13 (75%) TVöD-Bund

Application Deadline: The position will be published until it is successfully filled

Index number: 2026D-0647