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University Assistant (prae doc) at the Department of Meteorology and Geophysics
The University of Vienna (20 faculties and centres, 178 fields of study, approx. 9.800 members of staff, about 90.000 students) seeks to fill the position from 01.03.2020 of a
University Assistant (prae doc)
at the Department of Meteorology and Geophysics
Reference number: 10247
We offer a PhD (“prae-doctoral”) position in the field of inverse modelling. The position will be embedded in the new research group of Prof. Andreas Stohl on atmospheric transport modelling.
The starting date can be as early as 1 March 2020 and the duration is for four years.
The Department of Meteorology and Geophysics has recently filled two professorship positions (Andreas Stohl and Martin Weissmann, both starting in the beginning of 2020) and is currently in the process of filling a third professorship. This will create an exciting international working environment for many new scientists, large potential for collaboration, and the opportunity for strong personal development.
We seek a PhD student to work in the new research group of A. Stohl. The group will further develop the Lagrangian particle dispersion model FLEXPART (see www.flexpart.eu) and use it as its main working tool for a large range of applications, such as atmospheric energy and moisture transport, pollution transport, or inverse modelling of greenhouse gases.
Inverse modelling is a statistical method that can be used to infer the fluxes of gases or aerosols at the Earth’s surface based on atmospheric concentration or, for some species, deposition measurements. This requires an atmospheric transport model linking the fluxes and observations. For example, temporally and spatially sparse ice core records can be used to determine historical emissions of, e.g., black carbon or heavy metals on the continental scale. Currently still extremely sparse measurements of microplastics in the environment can be used to determine the origins of these plastics, which are currently almost entirely unknown. Or the very dense measurements of greenhouse gases can be used to determine their fluxes at high time and space resolution. The PhD student will apply inverse modelling in one or two research fields such as those described above, collaborating closely with internationally leading measurement groups.
Besides research, the successful candidate is also expected to participate in teaching and the supervision of students. Financial support is available for attending international meetings and visiting international research partners.
Participation in research, teaching and administration:
• Inverse modelling of trace constituents;
• Scientific publications and presentations;
• Participation in teaching and independent teaching of courses as defined by the collective agreement;
• We expect the successful candidate to sign a doctoral thesis agreement within 12-18 months.
• MSc or Diploma degree in meteorology, physics, mathematics, or related subject;
• Good communication skills and ability to work in a team;
• Experience in programming;
• Excellent command of written and spoken English.
Furthermore, the following skills/expertise will be an asset:
• Experience with use of atmospheric (transport) models;
• Good knowledge of statistics;
Your application should be a single PDF file that includes:
• A letter of motivation;
• An academic curriculum vitae;
• A list of publications and presentations, as well as evidence of teaching experience (if available);
• Contact information of two references;
• Degree certificates.
Main research field
Special research fields
Applications including a letter of motivation (German or English) should be submitted via the Job Center to the University of Vienna (
http://jobcenter.univie.ac.at) no later than 15.01.2020, mentioning reference number 10247.
For further information please contact firstname.lastname@example.org.
The University pursues a non-discriminatory employment policy and values equal opportunities, as well as diversity (
http://diversity.univie.ac.at/). The University lays special emphasis on increasing the number of women in senior and in academic positions. Given equal qualifications, preference will be given to female applicants.