Training Specialist - Machine Learning

Job reference: VN23-63

Salary and Grade: Grade A2 GBP 71,451 (Reading/UK) or EUR 86,824 (Bonn/Germany) NET annual basic salary + other benefits

Deadline for applications: 04/02/2024

Department: Forecast and Services

Location: Reading, UK or Bonn, Germany

Contract type: STF-PS

Publication date: 20/12/2023

Contract Duration: 2 years up to 31 May 2026, with possibility of further extensions

Your role 

ECMWF has an exciting opportunity for a Training Specialist (A2) responsible for learning and knowledge transfer focused on Machine Learning (ML). 
As part of ECMWF’s contribution to the Destination Earth (DestinE) initiative of the European Commission, you will work on defining and delivering ML training activities which support the transfer of knowledge and build capacity in the use of ML for data analytics and predictive modelling, leveraging the enhanced accuracy, resolution and processing speeds offered by the Digital Twins. 

The training activities will help prospective users leverage the full potential of DestinE ML capabilities as well as cover the broader political and societal context and ethical considerations of the use of ML for Earth System modelling. You will develop a training programme that targets technical users and will also support stakeholders along the ‘data value chain’ such as decision makers in agriculture, water, energy management and disaster risk reduction sectors. 

In this role you will have a unique opportunity to implement innovative training approaches and state-of-the-art learning technologies, to enhance user engagement and an impactful learning experience. Expected training includes, amongst others, a Massive Open Online Course (MOOC) and interactive online and in-person learning activities and educational resources. 

The Training Specialist is part of the User Outreach and Engagement Section and reports to the ECMWF Training Coordinator. You will work in collaboration with a wide range of colleagues across ECMWF: other training experts, partnership specialists, the communications and user services teams, scientists working on Digital Twins and ML developments, and experts focused on the AI driven version of ECMWF’s Integrated Forecasting System (AIFS). 

Externally you will work with ECMWF’s DestinE partners, ESA and EUMETSAT, service providers and ML experts in the National Meteorological and Hydrological Services of ECMWF Member States and Cooperating States as well as stakeholders and prospective users of the Digital Twins.

Travel may be required to deliver or support on-site/in-person training in Europe and to engage with ECMWF staff based in other duty stations. 

About ECMWF 

The European Centre for Medium-Range Weather Forecasts (ECMWF) is a world-leader in weather and environmental forecasting. As an international organisation we serve our members and the wider community with global weather predictions and data that is critical for understanding and solving the climate crisis. We function as a 24/7 research and operational centre with a focus on medium and long-range predictions, holding one of the largest meteorological data archives in the world. The success of our activities builds on the talent of our scientists and experts, strong partnerships with 35 Member and Co-operating States and the international community, some of the most powerful supercomputers in the world, and the use of innovative technologies and Machine Learning across our operations. ECMWF is a multi-site organisation, with a main office in Reading, UK, a data centre/supercomputer in Bologna, Italy, and a large presence in Bonn, Germany. ECMWF has also developed a strong partnership with the European Union and has been entrusted with the implementation and operation of the Destination Earth Initiative and the Climate Change and Atmosphere Monitoring Services of the Copernicus Programme. Other areas of work include High Performance Computing and the development of digital tools that enable ECMWF to extend provision of data and products covering weather, climate, air quality, fire and flood prediction and monitoring.

ECMWF is a multi-site organisation, with a main office in Reading, UK, a data centre/supercomputer in Bologna, Italy, and a large presence in Bonn, Germany. We appreciate the need for flexibility in the way our staff work. 

See  www.ecmwf.int for more info about what we do. 

The Destination Earth (DestinE) initiative...

ECMWF is one of the three entities entrusted to implement the DestinE initiative of the European Commission, alongside with ESA and EUMETSAT as partners. DestinE aims to deploy several highly accurate thematic digital replicas of the Earth, called Digital Twins. The Digital Twins will help monitor and predict environmental change and human impact, in order to develop and test scenarios that would support sustainable development and corresponding European policies for the Green Deal.  ECMWF is responsible for the delivery of these digital twins and of the Digital Twin engine, the software infrastructure needed to power them of some of Europe’s largest supercomputers, those of the European HPC Joint Undertaking (EuroHPC).  

The second phase of DestinE covers the period June 2024 – May 2026, and future phases are foreseen (subject to funding). Phase 2 will focus on early operations with consolidation, maintenance, and continuous evolution of the DestinE system components developed in the first phase. There will also be an enhanced focus on ML activities, including the deployment of workflows of components of a ML model for the Earth system, optimisation of the Digital Twin Engine to enable efficient model training and simulations, and other activities. One key element of the ML activities in phase 2 includes training. This shall build on recent ML training initiatives at ECMWF, including the Massive Open Online Course (MOOC) on ML for Weather and Climate (see https://learning.ecmwf.int/course/index.php?categoryid=1). 

For more information on DestinE, see https://ec.europa.eu/digital-single-market/en/destination-earth-destine and https://www.ecmwf.int/en/about/what-we-do/environmental-services/destination-earth 

...and Machine Learning at ECMWF

ECMWF is developing a world-leading, Machine Learning-based probabilistic weather forecasting system to complement our existing physics-based system. A range of novel Machine Learning approaches will be investigated for improved quality and efficiency of forecasts as part of ECMWF’s contribution to theDestination Earth initiative. A team of scientists and engineers in ECMWF are working collaboratively across the Centre and in our Member and Cooperating States to undertake this challenge. The position of User Support Specialist for Machine Learning will have a particular focus on provision of support for users and developers working in this domain.

Your responsibilities 

• Map specific ML-related training needs of DestinE users along the data value chain and across sectorial domains.
• Design and develop training activities in ML, in close collaboration with stakeholders, including a MOOC, e-learning material such as Jupyter notebooks, educational videos and podcasts and using methods such as gamification, aimed to create an engaging learning experience.
• Initiate procurements and manage contracts to implement training activities.
• Engage in other ECMWF training activities and other external training, and particularly those launched in scope of the Working Group on ML/AI in collaboration with ECMWF Member States.
• Ensure quality control of training activities and resources and maintenance of Machine Learning related training material.
• Collaborate with internal and external experts to develop training content as well as to define impactful training material and events, tailored to the needs of stakeholders and prospective users.
• Collect and analyse feedback from trainees/target audiences, address issues and communicate requirements to internal stakeholders and product owners.
• Track ML/AI training and user engagement needs to serve the European Commission’s Digital Agenda priorities
• Report and present to the European Commission on implemented and planned training activities and linked KPIs....

What we're looking for

• Excellent communication and presentation skills
• Eager to listen to users and share knowledge
• Excellent analytical and problem-solving skills with a proactive continuous improvement approach
• Take initiative and ability to work collaboratively with other ECMWF staff and DestinE partners, but also able to work independently
• Dedication, passion and enthusiasm to succeed both individually and across teams
• Highly organised with the capacity to work on a diverse range of tasks to tight deadlines

Education and Experience

• University degree (EQF Level 7 or above) in physics, mathematics, or any other relevant environmental science field.
• Experience in the planning, design and delivery of training and knowledge transfer activities and the management of training programmes.
• Knowledge of innovative learning design and technologies.
• Experience in learning needs assessment and evaluation of training programmes.
• Demonstrated experience in Machine Learning, and its application across various sectors.
• Understanding of the societal impact, including benefits, limitations and ethical considerations, of ML.

Skills and knowledge

• Good understanding of meteorological, atmospheric and climate science products and related services and their user communities.
• Exposure to programming, particularly in Python, is an asset.
• Familiarity with or knowledge in the use of Machine Learning libraries such as TensorFlow, Pytorch or Keras is an asset.
• Good understanding of High-Performance Computing (HPC) is an asset.
• Experience in handling large observational or modelling datasets and their statistical analysis is an asset.
• Candidates must be able to work effectively in English
• Knowledge of one of ECMWF’s other working languages (French or German) would be an advantage.

Other information 

Grade remuneration:  The successful candidates will be recruited at the A2 grade, according to the scales of the Co-ordinated Organisations. The position is assigned to the employment category STF-PS  as defined in the ECMWF Staff Regulations. Full details of salary scales and allowances available on the ECMWF website at www.ecmwf.int/en/about/jobs. 

Starting date:  As soon as possible

Candidates are expected to relocate to the duty station. As a multi-site organisation, ECMWF has adopted a hybrid organisation model which allows flexibility to staff to mix office working and teleworking, including away from the duty station (within the area of our member states and co-operating states).

Interviews by videoconference (MS Team) are expected to take place shortly after the closing date. 

Who can apply 

Applicants are invited to complete the online application form by clicking on the apply button below. 

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

In these exceptional times, we also welcome applications from Ukrainian nationals for this vacancy.  

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