See the job offer

France Energies Marines; Palaiseau; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A leading energy research organization in France is seeking a candidate to analyze atmospheric variables and assess the impacts of climate change using advanced statistical methods. The ideal candidate will hold a PhD in a relevant field and possess strong skills in mathematics, programming (Python or R), and bilingual communication. The role offers opportunities for collaborative work with expert teams and the chance to contribute to innovative research in the offshore wind industry.

Qualifications

• Experience in climate database management.
• Mastery of programming languages (Python or R).
• Good bilingual writing skills.

Responsabilités

• Analyze atmospheric variables from climate models.
• Evaluate climate models against atmospheric reanalyses.
• Assess the impact of climate change using different models.
• Develop multivariate statistical downscaling methods using ML.
• Work with engineers and researchers from multiple organizations.

Connaissances

Strong background in mathematics/statistics

Machine learning

Programming languages (Python or R)

Good writing skills (French and English)

Knowledge of Wind Energy

Bias correction or statistical downscaling methods

Rigor and scientific curiosity

Autonomy and organization

Communication in a partnership context

Ability to work in a group

Formation

PhD thesis in mathematics/statistics, machine learning, climate sciences, meteorology

Description du poste

The French offshore wind industry is growing rapidly (cumulative offshore wind capacity is expected to reach 3.6 GW by the end of 2027 with the commissioning of seven offshore projects) in a context that is certainly innovative and promising, but also highly competitive.

A thorough understanding of wind resources is one of the prerequisites for developing a robust business plan (Tobin et al., 2016 ; Devis et al., 2018 ; Tobin et al., 2018 ; Solaun and Cerda, 2019 ). The process, from initial design to decommissioning of an offshore wind farm, can last 40 years, not including repowering strategies. This long lifespan implies a potential change in wind resources between these two periods, possibly linked to climate change, and therefore a significant impact on the assessment of the levelized cost of energy (LCoE) (Hdidouan and Staffell, 2017 ).

Climate models are important tools for exploring these future climate change impacts and risks. However, they are not equally skillful in representing the climate processes that drive climate variability and change, particularly at regional scales(Eyring et al., 2019 ). It is, therefore, important to assess model performance and reliability over the historical period to gain confidence in future projections (Palmer et al., 2021 ).

The successful candidate will analyse atmospheric variables (wind speed, wind direction, temperature, turbulence, and atmospheric stability) from climate models, and assess the impacts of climate change on these variables and associated uncertainties. Two main tasks will be covered:

• Evaluation of climate models against atmospheric reanalyses and in-situ measurements.
• Assessment of the impact of climate change based on different climate models with a special focus on the sensitivity to spatial resolution and downscaling methods.

The successful candidate will also work on bias correction and statistical downscaling methods, that will be applied in a multivariate setting, allowing to adjust not only the different variables but also the dependence between these variables (Vrac, 2018 ; Robin et al., 2019 ). An additional major challenge for the candidate will be to develop multivariate statistical downscaling methods using Machine-Learning (ML) and Deep-Learning-based procedures, such as convolutional neural networks (Doury et al., 2024 ; Legasa et al., 2023 ), to improve the accuracy and reliability of climate projections for metocean parameters.

The successful candidate will be supervised by engineers and researchers from IPSL, EDF R&D, Total Energies, RTE and France Energies Marines, and will be based mainly in the IPSL and EDF offices in Saclay. Travel to the headquarters of France Energies Marines (Plouzané) is also expected.

Required Skills

Initial training

PhD thesis in mathematics/statistics, machine learning, climate sciences, meteorology

A first experience in climate database management is required

Specific knowledge

• Strong background in mathematics/statistics and/or machine learning

• Mastery of programming languages (Python or R)

• Good writing skills, in French and in English

Would be a +

• Knowledge of Wind Energy

• Knowledge of bias correction or statistical downscaling methods

•Rigor and scientific curiosity

• Autonomy, organization and proactive

• Ease of expression, argumentation and communication in a partnership context

• Ease of writing (scientific papers and thesis)

• Ability to work in a group in a multidisciplinary approach

In accordance with the regulations, priority will be given to people with disabilities who are equally qualified.

WARNING: If you are unable to submit your application via our website, please send it by e-mail [e-mail], specifying the job reference in the subject field.

• Starting date: Monday, January 5, 2026