"Postdoctoral Researcher in AI for Climate and Weather Forecasting"

Over the past few years, advances in computational power and the increasing availability of large volumes of remote sensing data with finer spatial and temporal resolutions have significantly transformed the way we approach climate and weather forecasting. These technological advancements have paved the way for innovations, such as the integration of artificial intelligence (AI) into real-time data analysis. This integration can revolutionize the interpretation of remote sensing data, allowing for rapid decision-making in critical situations, such as during natural disasters. AI models can process big datasets efficiently, helping to make informed, unbiased decisions, and ensuring resources are distributed to those most in need. Additionally, when combined with modeling experiments or enhanced research in parameterization, AI is accelerating computational processes, improving prediction accuracy, and enabling the creation of extensive model ensembles at a reduced cost.

In this context, we are looking for a highly motivated postdoctoral researcher to join our team at the CRSA to develop AI models, specifically deep learning approaches, to analyze and predict key climate variables such as precipitation and temperature which are essential for forecasting extreme events. These models will be applied to Morocco’s arid and semi-arid regions, aiming to produce more accurate predictions of droughts and floods. These enhanced climate and weather predictions will contribute to safeguarding food production, ensuring proactive water management strategies, and reducing the health risks associated with extreme weather conditions, particularly in rural areas heavily reliant on agriculture.

Key Duties

• Improve the accuracy of predicting climate variables like precipitation, temperature, and soil moisture by leveraging large multi-source datasets from remote sensing, IoT sensors, and climate models.
• Design and implement deep learning models for forecasting extreme weather events such as floods, droughts, and heatwaves, integrating probabilistic approaches to account for uncertainties.
• Use data assimilation techniques to combine observational data with AI models, improving real time forecasting accuracy.
• Collaborate with hydrologists, climatologists, and data scientists to ensure models are well grounded in scientific understanding.
• Design and conduct fieldwork to validate model predictions and improve the performance of deep learning approaches.
• Publish research findings in high-impact scientific journals and present the results at international conferences.
• Contribute to the supervision of Master’s and Ph.D students.

Qualifications

• Ph.D. in Artificial Intelligence, Data Science, Climate Science, Geoinformatics, Applied and Computational Mathematics, or related fields.
• Proven experience in developing deep learning models (e.g., CNNs, RNNs, LSTMs) for large-scale environmental data analysis.
• Familiarity with theoretical and practical aspects of scientific deep learning;
• Proficiency in programming languages such as Python and R.
• Strong experience working with climate and remote sensing datasets for environmental applications.
• Experience with high-performance computing and analysis of large datasets;
• A good track record of published research in peer-reviewed scientific journals.
• Ability to work in an interdisciplinary team and communicate scientific results effectively.