Research assistant (m/f/d) at the Institute of Meteorology, Faculty of Geosciences

The working group Troposphärische UmweltForschung (TrUmF), led by guest professors Martjin Schaap and Tim Butler, is concerned with model-based studies on:

• short-term forecasts of pollutant levels,
• source attribution of particulate matter and ozone, and
• pollutant inputs into ecosystems

A large part of the work is application-oriented and is carried out in real time. The TrUmF working group is involved in various research and development projects funded by the German Federal Ministry of Education and Research (BMBF), the EU, the German Environment Agency and various state environmental agencies or research institutions. In teaching, the working group focuses on the fundamentals of atmospheric chemistry and air pollution control, as well as air pollutant modelling. There is also close cooperation with the working group “Climate and air quality” at TNO in Utrecht (NL), particularly within joint projects on the application and further development of the LOTOS-EUROS chemical transport model, and with the working group “Air Quality Modelling for Policy Advice” at the RIFS Potsdam.

The DFG-funded project EACH (Artificial Intelligence Emulating Atmospheric Chemistry) is a joint project between the FU Berlin, the Max Planck Institute for Chemistry in Mainz, and the Barcelona Supercomputing Centre. The overarching goal of the EACH project is to develop innovative machine learning models able to emulate the complex atmospheric chemistry, accelerate its numerical solving in models and improve our understanding of the role of the different chemical processes at stake.

Job description:
Within the framework of the EACH project we are looking for a motivated person to perform numerical model simulations of atmospheric chemistry in order to produce a training dataset for the development of the physically constrained machine learning models, which will be led by our project partners. The successful applicant will contribute to the development and training of the machine learning models and will perform a physics-oriented evaluation of these models. Finally, the successful applicant will apply the machine learning models together with numerical model output and observational data to understand the sensitivity of secondary air pollutants (especially ozone) to various precursor gases and environmental factors.

Prof. Butler and Prof. Schaap will support the successful applicant in writing a doctoral dissertation in the field of atmospheric chemistry based on the work carried out in this project.

Requirements:
A completed master’s degree in meteorology, atmospheric physics, atmospheric chemistry, mathematics, computer science, or similar.

Desirable:
Knowledge of atmospheric chemistry. Ability to program in languages such as FORTRAN and Python. Experience with numerical modelling of atmospheric chemistry. Experience with machine learning techniques.

For further information, please contact Prof. Dr. Tim Butler (tim.butler@fu-berlin.de).

Applications should be sent by e-mail, together with significant documents, indicating the reference code, no later than December 2nd, 2024, in PDF format (preferably as one document) to Prof. Dr. Tim Butler: tim.butler@fu-berlin.de or postal to

Freie Universität Berlin
Fachbereich Geowissenschaften
Institut für Meteorologie
TrUmF (Arbeitsgruppe Troposphärische UmweltForschung)
Prof. Dr. Tim Butler
Carl-Heinrich-Becker-Weg 6-10
12165 Berlin (Steglitz)

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Freie Universität Berlin is an equal opportunity employer.