Your browser is outdated. We recommend an update or using another browser to visit our website.

Ihr Browser ist veraltet. Wir empfehlen Ihnen ein Update oder einen anderen Browser zum Besuch unserer Website.
 

Research fellow (postdoc, 1 years, 1.0 Stelle E13)

At the research group Geoinformatics - spatial big data

Expected starting time:

04-2022

Research fellow (postdoc, 1 years, 1.0 Stelle E13) at the research group 

Geoinformatics – spatial big data

Topic:

Air pollution modelling and health: spatiotemporal statistical modelling, model optimization, and the change of support problem.

Scientific contribution:

Poor ambient air quality represents one of the largest environmental risks to public health. Epidemiological studies, risk assessment, and urban planning require the quantification of long-term personal exposures over a large population. This brings challenges in detailed spatiotemporal mapping and the change of support problem when linking the air pollution concentration to, for example, health outcomes.

The research fellow will firstly focus on the development of statistical and machine learning models for spatiotemporal mapping and the air pollutants NO2 and Ozone. The statistical modelling approach predicts at unknown target spatial locations and times by capturing relationships between response and covariates, modelling the spatial or spatiotemporal processes of the observations and covariates, and learning features from remote sensing images. Spatiotemporal scales are important considerations in air pollution modelling. Specific challenges to be addressed are assimilating data of different sources, modelling of spatially heterogeneous response-covariate relationships, uncertainty assessment, and efficient computation. Then, the research fellow will investigate how to relate the predicted air pollution concentrations to personal exposures and health outcomes and socioeconomic variables. Specifically, if we could, and how to optimise a pollution concentration prediction model with different supports of the health outcomes or socioeconomic variables? 

Requirement:

  • Research background in geoinformatics, statistics, computer science, or equivalent.
  • Expertise in geostatistics, spatial statistics, big geocomputing, or machine learning.
  • Have a interest in atmospheric science, environmental modelling, or Geohealth.
  • Programming skills in R or Python.
  • Good English writing and communication skill.


The University of Bayreuth considers the diversity of its employees an enrichment, and explicitly commits itself to the goal of equal opportunity among the genders. Women are especially encouraged to apply. Applicants with children are likewise very welcome. The University of Bayreuth is a member of the Best Practice Club “Family in the University” and participated successfully in an audit by the Conference of University Rectors (HRK) on “Internationalization of Universities”. People with severe disabilities are considered with preference in cases of equal qualification.

The University of Bayreuth views the diversity of its staff as an asset and is expressly committed to the goal of gender equality. Women and persons who can help make the research and teaching profile of the university more diverse are strongly encouraged to apply. Applicants with children are highly welcome. The University of Bayreuth is a member of the best practice club Family at University, and an extended audit conducted by the German Rectors’ Conference (HRK) returned a favourable review for the University of Bayreuth’s commitment to internationalization. All qualifications being equal, applicants with disabilities will be given priority.


Your application


Please apply online via the Applicant portal Uni Bayreuth selecting “Geoinformatics” from the drop-down menu. Your application should contain a cover letter (describing your past and current research interests), your CV, your publication list as well as contact information of two references.

The documents will be deleted after the position has been filled in accordance with data protection requirements.

For further information please contact JProf. Dr. Meng Lu:  Meng.lu@uni-bayreuth.de