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Machine Learning Scientist (H/F)

ELECTRICITE DE FRANCE· Brighton, Royaume-Uni· repérée il y a 22 jours
IT & Data
Lieu
Brighton, Royaume-Uni
Durée
12 mois
Indemnité
2732 € / mois
Début
décembre 2026
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La mission

The Digital Innovation team is looking for a Machine Learning Scientist to contribute to new and existing machine learning, data science and digital projects in collaboration with business units across EDF. We are a team of 16 software engineers, data scientists and AI experts within EDF UK Research & Development that focus on exploring and implementing modern digital technologies within the EDF UK business. Our job is to not only keep up to date on the most cutting-edge digital technologies, but also to turn those technologies into technical solutions that bring value to the business. Your role will focus on the application of machine learning within the business, understanding business needs, working with external partners and collaborating with other members of the team and those across EDF Group R&D to conduct research into emerging topics and contribute to the development of digital solutions. This would include, for example, contributing to on-going projects such as defect-detection for wind turbine blades, extracting insights from smart meter data and exploring the use of large language models. You will work on the following topics: ● Developing deep learning algorithms ● Exploring and implementing language models across various use cases throughout EDF ● Experimenting and implementing state of the art convolutional deep neural networks ● Experimenting state of the art deep learning processings: time series analysis, advanced text mining or physical modelling ● Collaborating with developers and data scientist to deliver integrated projects ● Innovation and technology monitoring, looking for applications of recent research You’ll be equipped with a MacBook Pro and access to your own favourite tools (you choose), a collaborative open environment and an endless supply of new ideas and technologies. We are constantly learning: with monthly half-day skill sharing sessions along with hack days and meetups.