COLCOM- Postdoc in Wind Energy Modeling 13524

The Moroccan government has implemented a comprehensive strategy to promote the

development of renewable energy and enhance energy efficiency. This strategy actively supports the growth of renewable energy sources and their efficient utilization. As part of these efforts, Morocco aims to significantly increase its installed wind energy capacity from 280 MW in 2010 to 2,000 MW by 2020, as outlined in the Moroccan Integrated Wind Energy Project.

With ongoing economic and demographic growth, Morocco's energy demand is projected to triple by 2030, highlighting an urgent need for sustainable energy solutions. Morocco benefits from favorable climatic and geographical conditions for wind energy development. Seventeen regions have been identified for wind power generation, and the country's 3,500 km coastline

offers high wind speeds of up to 10 m/s. The future energy system will rely on large offshore wind farm clusters consisting of hundreds of wind turbines. These adjacent clusters will interact, with the energy yield of downstream turbines depending on the dynamic characteristics of the wake from upstream clusters. Accurate weather forecasting will require incorporating these wind farms into operational models.

About UM6P:

Located at the heart of the future Green City of Benguerir, Mohammed VI Polytechnic University (UM6P), a higher education institution with an international standard, is established to serve Morocco and the African continent. Its vision is honed around research and innovation at the service of education and development. This unique nascent university, with its state-of-the-art campus and infrastructure, has woven a sound academic and research network, and its recruitment process is seeking high quality academics and professionals in order to boost its quality-oriented research environment in the metropolitan area of Marrakech.

About COLCOM:

The School of Computer Science at Mohammed VI Polytechnic University (UM6P), Benguerir, Morocco, is seeking a postdoctoral candidate in the area of federated learning and wireless communications. The candidate must hold (or about to complete) a PhD in the related fields. The candidate will be involved in a two-year research project in collaboration with international academic partners. The main objective of the project is to study the interaction between machine learning and wireless communication fields. The successful candidate will answer questions such as how to assign limited communication resources to train the federated machine learning model efficiently. She/he will investigate realistic scenarios including non-iidness of data distribution, system heterogeneity, and dynamic environments.

Job Description

The postdoctoral researcher will contribute to a joint project involving industry partners and researchers from MIT. The primary objective is to efficiently map wind turbine responses to varying environmental conditions using fast and accurate surrogate models, establish reliability models, and quantify uncertainties from wind climate assessments to turbine load effects.

Development will be based on data from advanced large-eddy simulations (LES), with comparisons to measured data for quality control and uncertainty assessment.

Key Responsibilities:

• Conduct large-eddy simulations of various marine atmospheric boundary layers and their interactions with large offshore wind farm clusters.

• Validate LES models through benchmark comparisons.

• Derive budgets for higher-order turbulence moments from LES data.

• Develop new wind farm parameterizations for mesoscale models using higher-order turbulence closure schemes.

• Validate mesoscale models with the new parameterizations by comparing them to LES results.

Candidate Profile

• A Ph.D. in Physical Sciences, Meteorology, Engineering Physics, Mechanical Engineering (with a focus on fluid dynamics), or Wind Energy.

• Strong background in numerical simulations of atmospheric flows.

• Proficiency in statistical analysis of large datasets.

• High motivation and the ability to collaborate on applied research projects.

• Excellent English communication skills.

• Experience with mesoscale simulations.

We encourage applications from candidates passionate about renewable energy and atmospheric sciences, eager to contribute to cutting-edge research with real-world applications.