Scientist for Machine Learning

ECMWF - European Centre for Medium-Range Weather Forecasts

Scientist for Machine Learning

ECMWF - European Centre for Medium-Range Weather Forecasts

Reading or Bonn

1. Position information

Vacancy No.: VN21-02
Department: Research
Grade: A2
Job Ref. No.: STF-C/21-02
Reports to: AI and Machine Learning Coordinator
Closing Date: 10 March 2021

2. About ECMWF

ECMWF is both a research institute and a 24/7 operational service, producing numerical weather predictions for its Member and Co-operating States as well as users around the world. ECMWF carries out scientific and technical research and analysis aiming to continuously improve global prediction. ECMWF processes in its high-performance computing facility large amounts of observations to provide up-to-date global analyses and climate reanalyses of the atmosphere, ocean and land surface. For details, see

Over the years, ECMWF’s partnership with the European Union has grown, and in 2014 ECMWF became an entrusted entity to operate the Copernicus Atmosphere Monitoring Service (CAMS) and the Copernicus Climate Change Service (C3S) on behalf of the European Commission until mid- 2021 and ECMWF is preparing plans for the next phase of the Copernicus Programme for the period 2021-2027.

ECMWF currently operates from its headquarters, located in Reading, UK, and its data centre located in Bologna, Italy. Over the course of 2021, ECMWF will be opening additional new premises in Bonn, Germany.

ECMWF has embarked on an exciting new initiative to explore the use of artificial intelligence and machine learning in applications of numerical weather predictions. To learn more about future plans for machine learning at ECMWF, please have a look at our machine learning roadmap: and learning-ecmwf-roadmap-next-10-years. To learn more about the application of machine learning in the weather and climate domain in general, please refer to the webpage of the Machine Learning Seminar Series at ECMWF ( or the recordings of the ESA-ECMWF machine learning workshop ( system-observation-and-prediction).

3. Summary of the role

The successful candidate will play an essential role in the development of customised machine learning applications for several application areas at ECMWF. This will involve much interaction with domain scientists in providing both advice and support in the development and implementation of machine learning tools, and the efficient link between new machine learning tools and the existing numerical weather prediction workflow. These tasks will be supported by existing efforts at ECMWF to establish an efficient machine learning workflow for weather and climate models including the CliMetLab tool (, the new Centre of Excellence between ATOS and ECMWF ( excellence-weather-climate-modelling), and the MAchinE Learning for Scalable meTeoROlogy and climate (MAELSTROM) EuroHPC Joint Undertaking project which is coordinated by ECMWF.

4. Main duties and key responsibilities

  • Advising and supporting scientists during the development of machine learning applications at ECMWF with a focus on deep learning applications;
  • Assembling, curating and disseminating reference datasets in support of the machine learning activities;
  • Assuring reproducibility for machine learning solutions that are developed in collaboration with external partners;
  • Exploring potential machine learning use cases for operational use at ECMWF in collaboration with ECMWF staff and external partners.

5. Personal attributes

  • Strong interpersonal and communication skills, particularly listening to and respecting the views of others;
  • Enthusiasm to tackle challenging research questions when working with a complex computer model and willingness to learn new algorithms and tools;
  • Ability to work in a team at ECMWF, the member states and with external partners towards a common goal in interdisciplinary research projects;
  • Excellent analytical and problem-solving skills with an independent and proactive approach, together with an interest in identifying, investigating and solving technical challenges.

6. Qualifications and experience required


  • A university degree, or equivalent, in a discipline related to computer science, meteorology, physics, mathematics, machine learning or engineering is required;
  • A PhD in a related subject is desirable but not essential.


  • Experience in developing complex codes, parallel computing environments and high-performance computing facilities;
  • Experience working with machine learning applications (in particular deep- learning and neural networks);
  • Experience working with global atmosphere and/or ocean models or numerical models in computational fluid dynamics would be advantageous;
  • Experience with using Python for meteorological data, in particular machine libraries such as TensorFlow, would be advantageous.

Knowledge and skills (including language)

  • Good knowledge about data analytics and machine learning concepts. Proven ability to work in a Linux-based environment;
  • Good knowledge of Python and/or Jupiter notebooks;
  • Good knowledge of code versioning and reproducible coding would be advantageous;
  • Good knowledge of at least one high-level programming language such as C++ or Fortran and parallel programming (e.g., MPI and OpenMP) would be advantageous;
  • 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. The annual basic salary if based in the UK will be £62,166.00 net of tax. The annual basic salary if based in Germany will be EURO 75,178.92 net of tax. This position is assigned to the employment category STF-C as defined in the Staff Regulations.

Full details of salary scales and allowances are available on the ECMWF website at, including the Centre’s Staff Regulations regarding the terms and conditions of employment.

Starting date: As soon as possible.
Length of contract: Four years, with the possibility of a further contract.
Location: The role can be located in the Reading area, in Berkshire, United Kingdom, or at ECMWF’s duty station in Bonn, Germany. With the duty station in Bonn currently expected to open in summer 2021, the successful candidate may be asked to start in Reading initially.

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

Videoconference interviews (via Blue Jeans) are expected to take place at the end March 2021.

8. How to apply

Please complete the online application form available at:

To contact the ECMWF Recruitment Team, please email

Please refer to the ECMWF Privacy Statement. For details of how we will handle your personal data for this purpose, see:

At ECMWF, we consider an inclusive environment as key for our success. We are dedicated to ensuring a workplace that embraces diversity and provides equal opportunities for all, without distinction as to race, gender, age, marital status, social status, disability, sexual orientation, religion, personality, ethnicity and culture. We value the benefits derived from a diverse workforce and are committed to having staff that reflect the diversity of the countries that are part of our community, in an environment that nurtures equality and inclusion.

Staff are usually recruited from among nationals of the following Member States and Co-operating States:

Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland France, Hungary, Germany, Greece, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Morocco, the Netherlands, North Macedonia, Norway, 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.

Share this Job

© 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

Newsletter | Recruit | Advertise | Privacy | Contact Us

© 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

This website uses cookies to make your experience better. Continued use of this website means you accept our cookie policy.  Accept Cookies