Scientist for Machine Learning

1. Position information

Vacancy No.: VN20-22
Grade: A2
Job Ref. No.: STF-PS/20-22
Department: Computing Department
Section: HPC/Applications Team
Reports to: AI4Copernicus ECMWF Project Manager
Closing Date: 31 January 2021

2. About ECMWF

ECMWF is the leading centre for global, medium-range weather predictions and is the host of the largest archive of weather data in the world. ECMWF is both a research institute and a 24/7 operational service, producing and disseminatingnumerical weather predictions to its Member States.ECMWF has also been entrusted to operate the Copernicus Atmosphere Monitoring Service (CAMS) and the Copernicus Climate Change Service (C3S) on behalf of the European Commission.Every day, hundreds of millions of satellites and in situ observations are processed at ECMWF to provide the basis for up-to-date global analyses and climate reanalyses of the atmosphere, ocean and land surface, and to generate global weather predictions from hours up to a year ahead. To retain its world-leading position, ECMWF is performing cutting edge research in weather related sciences and high-performance computing. ECMWF’s weather forecasts are disseminated to the ECMWF Member States and thousands of users around the world.

For details, see www.ecmwf.int/.

3. Summary of the role

ECMWF has embarked on an exciting new initiative to explore the use of artificial intelligence and Machine Learning (ML) in applications of numerical weather predictions and provide the developed tools and techniques to the public. As part of this effort, ECMWF is participating in the AI4Copernicus H2020 project which funds this position.

This position will be in the Computing Department which coordinates ECMWF’s participation to the project. The successful candidate will apply their skills, knowledge, and expertise to help achieving the goals, and complete the deliverables of the AI4Copernicus project. The main focus will be on the development of supervised ML techniques such as Convolutional Neural Networks, Generative Adversarial Networks, Recurrent Neural Networks and Long-Short Term Memory (LSTM) networks that will be developed for the AI4Copernicus platform for the analysis of single-date and time series of remote sensing images to serve the user cases of AI4Copernicus in the area of agriculture, energy, security and health.

The main responsibility of ECMWF’s contribution is in the development of customised ML models relating to health and wellbeing. This includes predictions of pollution based on a mixture of local observations and three-dimensional data of the atmosphere using three dimensional convolutional neural networks as well as the detection of Earth Observation (EO) related features such as warm spells related to diseases such as Malaria.

The successful applicant will also contribute to knowledge extraction from EO data using unsupervised learning and will support open calls from AI4Copernicus. The Scientist will work in close collaboration with other teams across the organisation and strong communication skills are essential.

4. Main duties and key responsibilities

  • Developing supervised ML techniques for the analysis of single-date and time series of remote sensing images
  • Developing customised ML solutions pertaining to health and wellbeing in the context of Earth System science
  • Contributing to reports, dissemination and technology transfer activities of the AI4Copernicus project

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 complex technical tools and willingness to learn new algorithms, methodologies and methods
  • Ability to work in a team at ECMWF and within AI4Copernicus towards a common goal in an interdisciplinary research project
  • 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

Education

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

Experience

  • Experience in developing ML techniques and knowledge extraction.
  • Experience of ML techniques associated to Earth System data in general, and applications relating to health and wellbeing would be advantageous.
  • Experience with using Python for EO data, and in particular with machine libraries such as Tensor Flow or Keras, would also be advantageous.

Knowledge and skills (including language)

  • Experience of working in a Linux-based environment.
  • A good working knowledge and understanding of Cloud technologies and experience of working in Cloud (either community or public deployments) environments.
  • A good knowledge of Python and Jupyter notebooks would be useful.
  • 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

The successful candidate will be recruited at the A2 grade, according to the scales of the Co-ordinated 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-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 and conditions of employment.

Starting date: 1 March 2021, or as soon as possible thereafter.

Length of contract: 22 months, subject to available funding with possibility of extension.

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 start in Reading initially.

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

Interviews for this position are expected to take place virtually, in early February 2021.

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.

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.

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.

published: 18 December 2020     Please mention EARTHWORKS when responding to this advertisement.