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Post-Doc Hydrological modeling for flood early warning

Post-Doc Hydrological modeling for flood early warning


European Commission; Joint Research Centre

Ispra, Italy


Relevant division
Hydrological Sciences (HS)

Full time



Preferred education

Application deadline
20 January 2020

9 January 2020

Job description

The current vacancy is within the Copernicus Emergency Management Service focusing on the Global Flood Awareness Systems (GloFAS).

We are looking for a hydrological model developer that will perform research and development to improve GloFAS especially through the improvement of the hydrological model LISFLOOD and by taking into account feedback from key end-users such as the Emergency Response Coordination Centre. He/she will be part of the team that is responsible for policy support in the field of flood risk management as well as the management and further evolution of the Copernicus Emergency Management Service.


  • Evaluating how processes and methods (e.g. routing, infiltration, snow melt, reservoirs, etc.) in the open-source hydrological model LISFLOOD can be further improved especially with a view on improving the global hydrological simulations;
  • Assess whether the integration of additional data (e.g. from earth observation or other relevant sources) can improve the global hydrological LISFLOOD simulations
  • Improve the open-source calibration tool and perform a LISFLOOD model calibration for the global set up
  • Contribute to the scientific output through peer reviewed publications
  • Support the organisation of the annual Global Flood Partnership meetings

Required skills/qualifications:

  • The ideal candidate must have a degree in a relevant scientific area (atmospheric/geo/hydrologic /natural sciences, environmental engineering) together with a minimum of 3 years of research experience or a Ph.D.
  • Experience in using and developing large-scale hydrological models;
  • good programming (Python) and statistical / data analytical skills are essential.

Specific Requirements:

  • Experience in one or more of the following fields is an advantage: remote sensing, GIS, data assimilation and machine learning methods.
  • S/he should be a team player and be willing to learn and adapt to new tasks. Good communication skills (written and spoken) in English are expected (level C1).
  • The candidate should have a proven track record of peer reviewed scientific publications.