Scientist to Work on Machine Learning for Environmental Modelling

ECMWF - European Centre for Medium-Range Weather Forecasts

Scientist to Work on Machine Learning for Environmental Modelling

ECMWF - European Centre for Medium-Range Weather Forecasts

Reading or Bonn

1. Position information

Vacancy No.: VN21-44
Department: Forecast
Grade: A2
Section: Innovation Platform Team
Job Ref. No.: STF-PS/21-44
Reports to: Team Leader
Closing Date: 1 November 2021

2. About ECMWF

ECMWF is the European Centre for Medium-Range Weather Forecasts. It is an intergovernmental organisation created in 1975 by a group of European nations and is today supported by 34 Member and Co-operating States, mostly in Europe. The Centre’s mission is to serve and support its Member and Co-operating States and the wider community by developing and providing world-leading global numerical weather prediction. ECMWF functions as a 24/7 research and operational centre with a focus on medium and long-range predictions and holds one of the largest meteorological archives in the world. The success of its activities relies primarily on the talent of its scientists, strong partnerships with its Member and Co-operating States and the international community, some of the most powerful supercomputers in the world, and the use of innovative technologies such as machine learning across its operations.

Over the years, ECMWF has also developed a strong partnership with the European Union, and for the past seven years has been an entrusted entity for the implementation and operation of the Climate and the Atmosphere Monitoring Services of the EU Copernicus Programme, as well as a contributor to the Copernicus Emergency Management Service. The collaboration does not stop there and include other areas of work, including High Performance Computing and the development of digital tools. It is enabling ECMWF to now provide data and products covering weather, climate, ait quality, fire and flood prediction and monitoring. ECMWF has recently become a multi-site organisation, with its headquarters based since its creation in Reading, UK, its new data centre opening in 2021 in Bologna, Italy, and new offices, in Bonn, Germany.

For additional details, see: www.ecmwf.int.

3. Summary of the role

The Scientist will work in the Innovation Platform Team in the Forecast Department at ECMWF and will be responsible for developing new methods to upscale flood and fire forecast model system from catchment to European scale using machine learning techniques. The work will contribute to the H2020 project EIFFEL (rEvealIng the role oF geoss as the deFault digital portal for building climatEchange adaptation & mitigation appLications) and will utilize the forecast model systems used in EFFIS and EFAS within the Copernicus Emergency Management System in combination with GEOSS and Copernicus data to improve the operational model output. The Scientist will apply semantic machine-learning tools developed in the project to augment the forecast systems and assess potential improvements on the European scale. Such improvements can for example, involve improving background maps, incorporating processes such as irrigation or land use change, as well as improving the forcing observational data. They will also create impact response surfaces (IRS) over Europe using the model system to assess the sensitivity of the models with and without the augmentation. The IRS will then be used in the project for climate change impact assessment.

The successful applicant will be responsible for the implementing the developed methodology in ECMWF’s IT infrastructure and report the output in deliverables to the project as well as in international scientific journals. They will work closely with other teams in the Forecast Department responsible for the CEMS flood and fire services. They will also attend the EIFFEL workshops andproject meetings and interact with the other project partners to contribute to a successful project. This role will also involve interacting with ECMWF’s group on artificial intelligence to assess the wider use of semantic machine learning methods in other parts of ECMWF’s systems

4. Main duties and key responsibilities

  • Contributing to augmenting CEMS flood and fire model systems using machine learning methods and the available GEOSS and Copernicus data;
  • Running and evaluating experiments to assess the potential improvement of the models on the local and continental scale;
  • Upscaling the results to the continental scale and create impact response surfaces for the hydrologic and fire models;
  • Contributing to the project deliverables that ECMWF are in involved in within the EIFFEL project;
  • Providing regular feedback to ECMWF on the potential use of the ML tools in ECWMF’s systems;
  • Representing ECMWF at external and international meetings and scientific conferences to present the outcome of the research.

For more information about the flood and fire models used in CEMS please refer to: https://emergency.copernicus.eu/.

5. Personal attributes

  • Excellent interpersonal and communication skills;
  • Flexibility, with the ability to adapt to changing priorities and user needs;
  • Dedication and enthusiasm to work independently and in a small team;
  • Good analytical and problem-solving skills with a proactive approach;
  • Scientific curiosity to explore innovative development opportunities;
  • Ability to communicate with and understand the complex requirements of scientists, engineers and professional staff.

6. Qualifications and experience required

Education

  • A university degree (EQF Level 6) or equivalent in physics, mathematics, meteorology or a similarly related subject is required.

Experience

  • Demonstrated experience in the fields of running computer-based experiments with geophysical applications and (ideally) using machine learning methods to augment these models;
  • Good knowledge of scientific programming languages including Fortran, Python and shell scripts as well as experience with supercomputers and Linux systems;
  • Some experience working with impact response surfaces using NWP applications would also be advantageous.

Knowledge and skills (including language)

  • Some knowledge of semantic machine learning techniques would be an advantage;
  • Knowledge of hydrological and fire forecasting systems would be desirable but not essential;
  • Candidates must be able to work effectively in English and interviews will be conducted in English;
  • A good knowledge of one of the Centre’s other working languages (French or German) would be an advantage.

7. Other information

Grade remuneration

The successful candidate will be recruited at the A2 grade, according to the scales of the Coordinated Organisations and the annual basic salary will be £62,166.00 net of tax (UK) or EUR 75,178.52 net of tax (Germany). This position is assigned to the employment category STF-PS as defined in the Staff Regulations.

Full details of salary scales and allowances are available on the ECMWF website at www.ecmwf.int/en/about/jobs, including the Centre’s Staff Regulations regarding the terms andconditions of employment.

Starting date: As soon as possible.
Length of contract: Contract of 24 months, with the possibility of extension, subject to funding.
Location: Reading, UK or Bonn, Germany.

Successful applicants and members of their family forming part of their households will bevexempt from immigration restrictions.

8. How to apply

Please apply by completing the online application form available at www.ecmwf.int/en/about/jobs. To contact the ECMWF Recruitment Team, please email jobs@ecmwf.int.

Please refer to the ECMWF Privacy Statement. For details of how we will handle your personal data for this purpose, see: https://www.ecmwf.int/en/privacy.

Applications are invited from nationals from ECMWF Member States and Co-operating States, listed below:

Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Hungary, Germany, Greece, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Morocco, the Netherlands, Norway, North Macedonia, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey and the United Kingdom.

Applications from nationals from other countries may be considered in exceptional cases

Don't forget to mention EuroScienceJobs when applying.

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© EuroJobsites 2021

EuroJobsites is a registered company number: 4694396 VAT number: GB 880 9055 04

Registered address: EuroJobsites Ltd, Unit 8, Kingsmill Business Park, Kingston Upon Thames, London, KT1 3GZ, United Kingdom

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