IEK-8 – Troposphäre
Area of research:
Local traffic emissions in urban areas provide the dominant share for city scale air pollution, while other contributions from adjacent regions may add substantially to background concentrations that need to be considered. On regional scales, the combination of atmospheric chemistry models with measurements by data assimilation is the standard technique to aid in improving atmospheric chemistry forecasts, and meanwhile even in emission source strength assessments. The target of this PhD project focuses on the investigation of a prediction model for inner city air quality by data analytics methods, such as deep learning.
The PhD candidate will develop, implement, and test data analytics methods (e. g. cluster analysis, support vector machines, deep learning) in order to derive inner city air quality forecasts from street canyon observations and regional scale air quality forecasts. The candidate must handle, merge and exploit large amounts of data from atmospheric transport models, observations and other valuable information. Urban pollution will be assessed and it will be merged with regional background pollution. The investigation of local traffic emission quantification will be a main contribution to this work.
M. Sc. degree in physics, mathematics, meteorology, or a related field Experiences in data science, big data analyses, or deep learning methods are of great advantage Good knowledge in software development using FORTRAN90 or Python Experiences on high performance computing (HPC) Strong interest in atmospheric physics and chemistry Excellent knowledge of written and oral English: TOEFL or equivalent evidence of English-speaking skills You are convincing with your confident attitude and good communication skills 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 A high level of scholarship as indicated, for example, by bachelor and master study transcripts and two reference letters
Outstanding scientific and technical infrastructure – ideal conditions for successfully completing a doctoral degreeUnique HDS-LEE graduate school programA highly motivated group as well as an international and interdisciplinary working environment at one of Europe’s largest research establishmentsChance of participating in (international) conferencesContinuous scientific mentoring by your scientific advisorFurther development of your personal strengths, e.g. via a comprehensive further training programPay in line with 100 % of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund)A contract for the duration of 3 years
Further information on HDS-LEE is available at: www.hds-lee.de/Forschungszentrum Jülich aims to employ more women in this area and therefore particularly welcomes applications from women.We also welcome applications from disabled persons.