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2 Stellenausschreibungen: Universität Wien - 2 University Assistant (Prae doc) at the Department of Meteorology and Geophysics

24.11.2021

The University of Vienna (20 faculties and centres, 179 fields of study, approx. 10.000 members of staff, about 90.000 students) seeks to fill 2 positions from 01.02.2022 on:

University Assistant (prae doc) at the Department of Meteorology and Geophysics - Reference number: 12460

University Assistant (prae doc) at the Department of Meteorology and Geophysics - Reference number: 12461


University Assistant (prae doc) at the Department of Meteorology and Geophysics - Reference number: 12460

We offer a PhD (“prae-doctoral”) position in the field of inverse modelling of greenhouse gases. The position will be embedded in the research group of Prof. Andreas Stohl on atmospheric transport modelling and will be part of the Vienna Network for Atmospheric Research (VINAR), a collaboration between the University of Vienna and the Austrian National Weather Service ZAMG (Zentralanstalt für Meteorologie und Geodynamik).

The starting date can be as early as 1 February 2022. The announcement is made for four years, whereby the employment relationship is initially limited to 1,5 years and is automatically extended to a total of four years, unless the employer submits a declaration of non-renewal after a maximum of 12 months.

Greenhouse gases are emitted by a variety of sources, both natural and anthropogenic. Countries are obliged to report their emissions to UNFCCC, for which they estimate these emissions using so-called bottom-up methods based on, e.g., energy statistics, or land use changes. However, such estimates especially for fluxes from natural lands, are highly uncertain and need independent verification.

The doctoral student will develop inverse methods that allow constraining the greenhouse gas fluxes based on atmospheric observation data, a transport model and prior emission information. The work shall use the Lagrangian particle dispersion model FLEXPART (see www.flexpart.eu), which is being developed in our group, and may build on the inversion framework FLEXINVERT (https://flexinvert.nilu.no/), also used in our group. While our past work has focussed on the use of ground-based observations, the doctoral student shall also develop new methods that are suitable for the use of the large greenhouse gas observation data sets produced by forthcoming satellite missions, thus facilitating unprecedented high-resolution emission estimates.

This position will be part of the Vienna Network for Atmospheric Research (VINAR), a collaboration between the University of Vienna and ZAMG (https://vinar.univie.ac.at/). The student will have access to data and models used at ZAMG and the research will be conducted in close collaboration with ZAMG.

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.

Duration of employment: 4 year/s

Extent of Employment: 30 hours/week
Job grading in accordance with collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) with relevant work experience determining the assignment to a particular salary grade.

Job Description:
Participation in research, teaching and administration:

  • Development and application of inverse methods for determining greenhouse gas fluxes;
  • Scientific publications and presentations;
  • Participation in teaching and independent teaching of courses as defined by the collective agreement;
  • Support of collaboration activities in the Vienna Network for Atmospheric Research;
  • We expect the successful candidate to sign a doctoral thesis agreement within 12 months.

Profile:

  • MSc or Diploma degree in meteorology, physics, mathematics, or related subject;
  • Good communication skills and ability to work in a team;
  • Large 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;
  • A curriculum vitae;
  • A list of publications and presentations;
  • Contact information of two references;
  • Degree certificates.

Research fields:

  • Main research field: Meteorology, Climatology
  • Special research fields: Meteorology
  • Importance: SHOULD

​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 31.01.2022, mentioning reference number 12460.

For further information please contact Stohl, Andreas +43-1-4277-53730.

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.

Human Resources and Gender Equality of the University of Vienna
Reference number: 12460
E-Mail: jobcenter@univie.ac.at
Privacy Policy of the University of Vienna


University Assistant (prae doc) at the Department of Meteorology and Geophysics - Reference number: 12461

We offer a PhD (“prae-doctoral”) position in the field of data assimilation for numerical weather prediction (NWP). The position will be embedded in the research group of Prof. Martin Weissmann and will be part of the Vienna Network for Atmospheric Research (VINAR), a collaboration between the University of Vienna and the Austrian National Weather Service ZAMG (Zentralanstalt für Meteorologie und Geodynamik).

The starting date can be as early as 1 February 2022. The announcement is made for four years, whereby the employment relationship is initially limited to 1,5 years and is automatically extended to a total of four years, unless the employer submits a declaration of non-renewal after a maximum of 12 months.

The incorporation of ensemble-based estimates of error covariances in the framework of hybrid data assimilation schemes has been one of the major contributions to the improvement of forecast skill in recent years. ZAMG is currently still running a 3D-Var assimilation scheme with climatological covariances, but is planning to implement and test a new hybrid ensemble-variational (EnVar) scheme in 2022 that is being developed within the international ACCORD NWP (http://www.umr-cnrm.fr/accord) consortium (mainly by Météo-France). The EnVar scheme is coupled with the convection-permitting NWP model AROME that is also used operationally by ZAMG.

The doctoral student will test this experimental EnVar scheme and develop improved approaches for the localization of horizontal and vertical ensemble-based covariance estimates. The development will be based on both simplified assimilation experiments with the experimental EnVar scheme as well as very large ensemble simulations with 1000 ensemble members that were recently conducted in collaboration with the RIKEN institute in Japan.

This position will be part of the Vienna Network for Atmospheric Research (VINAR), a collaboration between the University of Vienna and ZAMG (https://vinar.univie.ac.at/). The student will have access to data and models used at ZAMG and the research will be conducted in close collaboration with ZAMG.

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.

Duration of employment: 4 year/s

Extent of Employment: 30 hours/week
Job grading in accordance with collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) with relevant work experience determining the assignment to a particular salary grade.

Job Description:
Participation in research, teaching and administration:

  • Development of improved approaches for the localization of ensemble-based covariance estimates and assimilation experiments with the experimental EnVar scheme for the NWP model AROME.
  • Scientific publications and presentations;
  • Participation in teaching and independent teaching of courses as defined by the collective agreement;
  • Support of collaboration activities in the Vienna Network for Atmospheric Research;
  • We expect the successful candidate to sign a doctoral thesis agreement within 12 months.

Profile:

  • MSc or Diploma degree in meteorology, physics, mathematics, or related subject;
  • Good communication skills and ability to work in a team;
  • Large 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;
  • A curriculum vitae;
  • A list of publications and presentations;
  • Contact information of two references;
  • Degree certificates.

Research fields:

  • Main research field: Meteorology, Climatology
  • Special research fields: Meteorology
  • Importance: SHOULD

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 31.01.2022, mentioning reference number 12461.

For further information please contact Weißmann, Martin +43-1-4277-53710.

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.

Human Resources and Gender Equality of the University of Vienna
Reference number: 12461
E-Mail: jobcenter@univie.ac.at
Privacy Policy of the University of Vienna